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Hate speech (HS) datasets, however, have traditionally been developed by language. This hides potential cultural biases, as one language may be spoken in different countries home to different cultures. In this work, we evaluate cultural bias in HS datasets by leveraging two interrelated cultural proxies: language and geography. We conduct a systematic survey of HS datasets in eight languages and confirm past findings on their English-language bias, but also show that this bias has been steadily decreasing in the past few years. For three geographically-widespread languages{---}English, Arabic and Spanish{---}we then leverage geographical metadata from tweets to approximate geo-cultural contexts by pairing language and country information. We find that HS datasets for these languages exhibit a strong geo-cultural bias, largely overrepresenting a handful of countries (e.g., US and UK for English) relative to their prominence in both the broader social media population and the general population speaking these languages. Based on these findings, we formulate recommendations for the creation of future HS datasets.", +} \ No newline at end of file diff --git a/publication/2024-hate-geographies/featured.png b/publication/2024-hate-geographies/featured.png new file mode 100644 index 0000000000000000000000000000000000000000..ec40f16d04110012764fd42fa0ecb6a60a629fee GIT binary patch literal 757118 zcmeEug(V=0z5trLgB#0yum&0N? z38Q^UhfN>{!V=Y{GPe{o8s`gR(Br+kz*3VZVCeAD&=mFoH?Dcng69~j(Jy+e2 zJssDZ?h`N96CKy*YfzCIqS(HzrGH0Skv#&JwA|(Q^t@nJ<4Z+ROhukW3T2Cs7Z@I1 zM2cBF+h09Z^kJH@#FH!VKD(bte@5Jcjzl|5kd3qYSvUwE)u#$8v(y7ANUb|zs5kbd zJz2XT=LT&%k6dDID~nuWVXGE~^%AItKn&R@e{j8z6ZzMMpKzXd1h&%#`}>8bgu%}U zt|fM&V)-Po4(WreQ*jfc3P&N?-dRzXd;=$hilSnUXn7`N%qvJGdf)p}j=g^@d&k!6 zbsSKR7ypFxyf*p9(N-_g6HoY~eT$&!Tc)&>L_RK3a7cjPnOuLrn*Yxzq zI0EIb>z#+t3vD_vT7ZdU5ZBY1$VExsw`5DSS-AZ)@975DJ0$Zl?L%d#fqbTpa=Z+m zT;>zVI9VY<`)hp|o+?}3nk!H3-d)9giV5P&3>P3V>tK{n2;ff@PXuC@V6;abB~};H zC5*N*+pH9aDiG$uI#5jFWr%i)M!I^rOeIPr&+3j>+Dbs#rS0|IbRIlnuSDN`d$se$ 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+ + + + + + + + + From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets | MilaNLP Lab @ Bocconi University + + + + + +

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    From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets

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    Abstract

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    Perceptions of hate can vary greatly across cultural contexts. Hate speech (HS) datasets, however, have traditionally been developed by language. This hides potential cultural biases, as one language may be spoken in different countries home to different cultures. In this work, we evaluate cultural bias in HS datasets by leveraging two interrelated cultural proxies: language and geography. We conduct a systematic survey of HS datasets in eight languages and confirm past findings on their English-language bias, but also show that this bias has been steadily decreasing in the past few years. For three geographically-widespread languages – English, Arabic and Spanish – we then leverage geographical metadata from tweets to approximate geo-cultural contexts by pairing language and country information. We find that HS datasets for these languages exhibit a strong geo-cultural bias, largely overrepresenting a handful of countries (e.g., US and UK for English) relative to their prominence in both the broader social media population and the general population speaking these languages. Based on these findings, we formulate recommendations for the creation of future HS datasets.

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    WOAH at NAACL 2024
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    Related

    diff --git a/publication/2024-politicalcompass/index.html b/publication/2024-politicalcompass/index.html index 67c7fca4..3fa06d06 100644 --- a/publication/2024-politicalcompass/index.html +++ b/publication/2024-politicalcompass/index.html @@ -1147,15 +1147,15 @@

    Related

    diff --git a/publication/2024-prism/cite.bib b/publication/2024-prism/cite.bib new file mode 100644 index 00000000..41d57b39 --- /dev/null +++ b/publication/2024-prism/cite.bib @@ -0,0 +1,6 @@ +@inproceedings{kirk2024prism, + title={The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models}, + author={Kirk, Hannah Rose and Whitefield, Alexander and R{\"o}ttger, Paul and Bean, Andrew Michael and Margatina, Katerina and Mosquera, Rafael and Ciro, Juan Manuel and Bartolo, Max and Williams, Adina and He, He and others}, + booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, + year={2024} +} \ No newline at end of file diff --git a/publication/2024-prism/featured.png b/publication/2024-prism/featured.png new file mode 100644 index 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    The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models

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    Abstract

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    Human feedback is central to the alignment of Large Language Models (LLMs). However, open questions remain about methods (how), domains (where), people (who) and objectives (to what end) of feedback processes. To navigate these questions, we introduce PRISM, a dataset that maps the sociodemographics and stated preferences of 1,500 diverse participants from 75 countries, to their contextual preferences and fine-grained feedback in 8,011 live conversations with 21 LLMs. With PRISM, we contribute (i) wider geographic and demographic participation in feedback; (ii) census-representative samples for two countries (UK, US); and (iii) individualised ratings that link to detailed participant profiles, permitting personalisation and attribution of sample artefacts. We target subjective and multicultural perspectives on value-laden and controversial issues, where we expect interpersonal and cross-cultural disagreement. We use PRISM in three case studies to demonstrate the need for careful consideration of which humans provide what alignment data.

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    Related

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  • Fair and Argumentative Language Modeling for Computational Argumentation
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  • Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions
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    + The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models +

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    + From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets +

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    + July, 2024 +
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    + Perceptions of hate can vary greatly across cultural contexts. Hate speech (HS) datasets, however, have traditionally been developed by … +
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    diff --git a/publication/index.xml b/publication/index.xml index f92dd12c..e8b33ded 100644 --- a/publication/index.xml +++ b/publication/index.xml @@ -5,13 +5,21 @@ https://milanlproc.github.io/publication/ Publications - Source Themes Academic (https://sourcethemes.com/academic/)en-us© MilaNLP, 2024Sun, 22 Sep 2024 00:00:00 +0000 + Source Themes Academic (https://sourcethemes.com/academic/)en-us© MilaNLP, 2024Tue, 03 Dec 2024 00:00:00 +0000 img/map[gravatar:%!s(bool=false) shape:circle] Publications https://milanlproc.github.io/publication/ + + The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models + https://milanlproc.github.io/publication/2024-prism/ + Tue, 03 Dec 2024 00:00:00 +0000 + https://milanlproc.github.io/publication/2024-prism/ + + + Divine LLaMAs: Bias, Stereotypes, Stigmatization, and Emotion Representation of Religion in Large Language Models https://milanlproc.github.io/publication/2024-divine-llamas-emotion-bias/ @@ -44,6 +52,14 @@ + + From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets + https://milanlproc.github.io/publication/2024-hate-geographies/ + Sat, 20 Jul 2024 00:00:00 +0000 + https://milanlproc.github.io/publication/2024-hate-geographies/ + + + XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models https://milanlproc.github.io/publication/2024-xstest/ diff --git a/publication_types/1/index.html b/publication_types/1/index.html index e3e63574..e8f3a09f 100644 --- a/publication_types/1/index.html +++ b/publication_types/1/index.html @@ -271,7 +271,7 @@ - + @@ -615,10 +615,10 @@

    1

    -

    Divine LLaMAs: Bias, Stereotypes, Stigmatization, and Emotion Representation of Religion in Large Language Models

    +

    The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models

    - Emotions play important epistemological and cognitive roles in our lives, revealing our values and guiding our actions. Previous work has shown that LLMs display biases in emotion attribution along gender lines. However, unlike gender, which says … + Human feedback is central to the alignment of Large Language Models (LLMs). However, open questions remain about methods (how), domains (where), people (who) and objectives (to what end) of feedback processes. To navigate these questions, we …
    @@ -627,10 +627,10 @@

    Divine LLaMAs: Bias
    -

    Compromesso! Italian Many-Shot Jailbreaks Undermine the Safety of Large Language Models

    +

    Divine LLaMAs: Bias, Stereotypes, Stigmatization, and Emotion Representation of Religion in Large Language Models

    - As diverse linguistic communities and users adopt large language models (LLMs), assessing their safety across languages becomes critical. Despite ongoing efforts to make LLMs safe, they can still be made to behave unsafely with jailbreaking, a … + Emotions play important epistemological and cognitive roles in our lives, revealing our values and guiding our actions. Previous work has shown that LLMs display biases in emotion attribution along gender lines. However, unlike gender, which says …
    @@ -639,10 +639,10 @@

    Compromesso! Italian Many-Shot Jai
    -

    My Answer is C: First-Token Probabilities Do Not Match Text Answers in Instruction-Tuned Language Models

    +

    Compromesso! Italian Many-Shot Jailbreaks Undermine the Safety of Large Language Models

    - The open-ended nature of language generation makes the evaluation of autoregressive large language models (LLMs) challenging. One common evaluation approach uses multiple-choice questions to limit the response space. The model is then evaluated by … + As diverse linguistic communities and users adopt large language models (LLMs), assessing their safety across languages becomes critical. Despite ongoing efforts to make LLMs safe, they can still be made to behave unsafely with jailbreaking, a …
    @@ -651,10 +651,10 @@

    My Answer is C: First-Token Probab
    -

    Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models

    +

    My Answer is C: First-Token Probabilities Do Not Match Text Answers in Instruction-Tuned Language Models

    - Much recent work seeks to evaluate values and opinions in large language models (LLMs) using multiple-choice surveys and questionnaires. Most of this work is motivated by concerns around real-world LLM applications. For example, politically-biased … + The open-ended nature of language generation makes the evaluation of autoregressive large language models (LLMs) challenging. One common evaluation approach uses multiple-choice questions to limit the response space. The model is then evaluated by …
    @@ -663,10 +663,10 @@

    Political Compass or Spinning
    -

    XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models

    +

    Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models

    - Without proper safeguards, large language models will readily follow malicious instructions and generate toxic content. This risk motivates safety efforts such as red-teaming and large-scale feedback learning, which aim to make models both helpful … + Much recent work seeks to evaluate values and opinions in large language models (LLMs) using multiple-choice surveys and questionnaires. Most of this work is motivated by concerns around real-world LLM applications. For example, politically-biased …
    @@ -675,10 +675,10 @@

    XSTest: A Test Suite for Identifying Ex
    -

    DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods

    +

    From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets

    - Social scientists increasingly use demographically stratified social media data to study the attitudes, beliefs, and behavior of the general public. To facilitate such analyses, we construct, validate, and release the representative DADIT dataset of … + Perceptions of hate can vary greatly across cultural contexts. Hate speech (HS) datasets, however, have traditionally been developed by language. This hides potential cultural biases, as one language may be spoken in different countries home to …
    @@ -687,10 +687,10 @@

    DADIT: A Dataset for Demographic Classif
    -

    Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions

    +

    XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models

    - Training large language models to follow instructions makes them perform better on a wide range of tasks, generally becoming more helpful. However, a perfectly helpful model will follow even the most malicious instructions and readily generate … + Without proper safeguards, large language models will readily follow malicious instructions and generate toxic content. This risk motivates safety efforts such as red-teaming and large-scale feedback learning, which aim to make models both helpful …
    @@ -699,10 +699,10 @@

    Safety-Tuned LLaMAs: Lessons From
    -

    Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution

    +

    DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods

    - Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, … + Social scientists increasingly use demographically stratified social media data to study the attitudes, beliefs, and behavior of the general public. To facilitate such analyses, we construct, validate, and release the representative DADIT dataset of …
    @@ -711,10 +711,10 @@

    Angry Men, Sad Wome
    -

    Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions

    +

    Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions

    - Emotions are a central aspect of communication. Consequently, emotion analysis (EA) is a rapidly growing field in natural language processing (NLP). However, there is no consensus on scope, direction, or methods. In this paper, we conduct a thorough … + Training large language models to follow instructions makes them perform better on a wide range of tasks, generally becoming more helpful. However, a perfectly helpful model will follow even the most malicious instructions and readily generate …
    @@ -723,10 +723,10 @@

    Emotion Analysis in NL
    -

    A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation

    +

    Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution

    - Recent instruction fine-tuned models can solve multiple NLP tasks when prompted to do so, with machine translation (MT) being a prominent use case. However, current research often focuses on standard performance benchmarks, leaving compelling … + Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, …
    diff --git a/publication_types/1/index.xml b/publication_types/1/index.xml index 5cbd715d..35d006aa 100644 --- a/publication_types/1/index.xml +++ b/publication_types/1/index.xml @@ -5,13 +5,21 @@ https://milanlproc.github.io/publication_types/1/ 1 - Source Themes Academic (https://sourcethemes.com/academic/)en-us© MilaNLP, 2024Sun, 22 Sep 2024 00:00:00 +0000 + Source Themes Academic (https://sourcethemes.com/academic/)en-us© MilaNLP, 2024Tue, 03 Dec 2024 00:00:00 +0000 img/map[gravatar:%!s(bool=false) shape:circle] 1 https://milanlproc.github.io/publication_types/1/ + + The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models + https://milanlproc.github.io/publication/2024-prism/ + Tue, 03 Dec 2024 00:00:00 +0000 + https://milanlproc.github.io/publication/2024-prism/ + + + Divine LLaMAs: Bias, Stereotypes, Stigmatization, and Emotion Representation of Religion in Large Language Models https://milanlproc.github.io/publication/2024-divine-llamas-emotion-bias/ @@ -44,6 +52,14 @@ + + From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets + https://milanlproc.github.io/publication/2024-hate-geographies/ + Sat, 20 Jul 2024 00:00:00 +0000 + https://milanlproc.github.io/publication/2024-hate-geographies/ + + + XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models https://milanlproc.github.io/publication/2024-xstest/ diff --git a/publication_types/1/page/2/index.html b/publication_types/1/page/2/index.html index d78c994e..1cefaf8f 100644 --- a/publication_types/1/page/2/index.html +++ b/publication_types/1/page/2/index.html @@ -271,7 +271,7 @@ - + @@ -615,10 +615,10 @@

    1

    -

    The Empty Signifier Problem: Towards Clearer Paradigms for Operationalising 'Alignment' in Large Language Models

    +

    Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions

    - In this paper, we address the concept of 'alignment' in large language models (LLMs) through the lens of post-structuralist socio-political theory, specifically examining its parallels to empty signifiers. To establish a shared vocabulary around how … + Emotions are a central aspect of communication. Consequently, emotion analysis (EA) is a rapidly growing field in natural language processing (NLP). However, there is no consensus on scope, direction, or methods. In this paper, we conduct a thorough …
    @@ -627,10 +627,10 @@

    The Empty Signifier Problem
    -

    The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values

    +

    A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation

    - Human feedback is increasingly used to steer the behaviours of Large Language Models (LLMs). However, it is unclear how to collect and incorporate feedback in a way that is efficient, effective and unbiased, especially for highly subjective human … + Recent instruction fine-tuned models can solve multiple NLP tasks when prompted to do so, with machine translation (MT) being a prominent use case. However, current research often focuses on standard performance benchmarks, leaving compelling …
    @@ -639,10 +639,10 @@

    The Past, Prese
    -

    Wisdom of Instruction-Tuned Language Model Crowds. Exploring Model Label Variation

    +

    The Empty Signifier Problem: Towards Clearer Paradigms for Operationalising 'Alignment' in Large Language Models

    - Large Language Models (LLMs) exhibit remarkable text classification capabilities, excelling in zero- and few-shot learning (ZSL and FSL) scenarios. However, since they are trained on different datasets, performance varies widely across tasks between … + In this paper, we address the concept of 'alignment' in large language models (LLMs) through the lens of post-structuralist socio-political theory, specifically examining its parallels to empty signifiers. To establish a shared vocabulary around how …
    @@ -651,10 +651,10 @@

    Wisdom of Instruction-Tun
    -

    A Multi-dimensional study on Bias in Vision-Language models

    +

    The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values

    - In recent years, joint Vision-Language (VL) models have increased in popularity and capability. Very few studies have attempted to investigate bias in VL models, even though it is a well-known issue in both individual modalities.This paper presents … + Human feedback is increasingly used to steer the behaviours of Large Language Models (LLMs). However, it is unclear how to collect and incorporate feedback in a way that is efficient, effective and unbiased, especially for highly subjective human …
    @@ -663,10 +663,10 @@

    A
    -

    MilaNLP at SemEval-2023 Task 10: Ensembling Domain-Adapted and Regularized Pretrained Language Models for Robust Sexism Detection

    +

    Wisdom of Instruction-Tuned Language Model Crowds. Exploring Model Label Variation

    - We present the system proposed by the MilaNLP team for the Explainable Detection of Online Sexism (EDOS) shared task. We propose an ensemble modeling approach to combine different classifiers trained with domain adaptation objectives and standard … + Large Language Models (LLMs) exhibit remarkable text classification capabilities, excelling in zero- and few-shot learning (ZSL and FSL) scenarios. However, since they are trained on different datasets, performance varies widely across tasks between …
    @@ -675,10 +675,10 @@

    Respectful or Toxic? Using Zero-Shot Learning with Language Models to Detect Hate Speech

    +

    A Multi-dimensional study on Bias in Vision-Language models

    - Hate speech detection faces two significant challenges: 1) the limited availability of labeled data and 2) the high variability of hate speech across different contexts and languages. Prompting brings a ray of hope to these challenges. It allows … + In recent years, joint Vision-Language (VL) models have increased in popularity and capability. Very few studies have attempted to investigate bias in VL models, even though it is a well-known issue in both individual modalities.This paper presents …

    @@ -687,10 +687,10 @@

    Respectful or
    -

    Temporal and Second Language Influence on Intra-Annotator Agreement and Stability in Hate Speech Labelling

    +

    MilaNLP at SemEval-2023 Task 10: Ensembling Domain-Adapted and Regularized Pretrained Language Models for Robust Sexism Detection

    - Much work in natural language processing (NLP) relies on human annotation. The majority of this implicitly assumes that annotator’s labels are temporally stable, although the reality is that human judgements are rarely consistent over time. As a … + We present the system proposed by the MilaNLP team for the Explainable Detection of Online Sexism (EDOS) shared task. We propose an ensemble modeling approach to combine different classifiers trained with domain adaptation objectives and standard …
    @@ -699,10 +699,10 @@

    The Ecological Fallacy in Annotation: Modeling Human Label Variation goes beyond Sociodemographics

    +

    Respectful or Toxic? Using Zero-Shot Learning with Language Models to Detect Hate Speech

    - Many NLP tasks exhibit human label variation, where different annotators give different labels to the same texts. This variation is known to depend, at least in part, on the sociodemographics of annotators. Recent research aims to model individual … + Hate speech detection faces two significant challenges: 1) the limited availability of labeled data and 2) the high variability of hate speech across different contexts and languages. Prompting brings a ray of hope to these challenges. It allows …
    @@ -711,10 +711,10 @@

    The State of Profanity Obfuscation in Natural Language Processing Scientific Publications

    +

    Temporal and Second Language Influence on Intra-Annotator Agreement and Stability in Hate Speech Labelling

    - Work on hate speech has made considering rude and harmful examples in scientific publications inevitable. This situation raises various problems, such as whether or not to obscure profanities. While science must accurately disclose what it does, the … + Much work in natural language processing (NLP) relies on human annotation. The majority of this implicitly assumes that annotator’s labels are temporally stable, although the reality is that human judgements are rarely consistent over time. As a …
    @@ -723,10 +723,10 @@

    The State of Pr
    -

    What about ''em''? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns

    +

    The Ecological Fallacy in Annotation: Modeling Human Label Variation goes beyond Sociodemographics

    - As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. Exclusion is particularly harmful in one of the most popular NLP applications, machine translation (MT). Wrong pronoun … + Many NLP tasks exhibit human label variation, where different annotators give different labels to the same texts. This variation is known to depend, at least in part, on the sociodemographics of annotators. Recent research aims to model individual …
    diff --git a/publication_types/1/page/3/index.html b/publication_types/1/page/3/index.html index 7ed5f10f..279290ec 100644 --- a/publication_types/1/page/3/index.html +++ b/publication_types/1/page/3/index.html @@ -271,7 +271,7 @@ - + @@ -615,10 +615,10 @@

    1

    -

    What about ''em''? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns

    +

    The State of Profanity Obfuscation in Natural Language Processing Scientific Publications

    - As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. Exclusion is particularly harmful in one of the most popular NLP applications, machine translation (MT). Wrong pronoun … + Work on hate speech has made considering rude and harmful examples in scientific publications inevitable. This situation raises various problems, such as whether or not to obscure profanities. While science must accurately disclose what it does, the …
    @@ -627,10 +627,10 @@

    -

    Leveraging Social Interactions to Detect Misinformation on Social Media

    +

    What about ''em''? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns

    - Detecting misinformation threads is crucial to guarantee a healthy environment on social media. We address the problem using the data set created during the COVID-19 pandemic. It contains cascades of tweets discussing information weakly labeled as … + As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. Exclusion is particularly harmful in one of the most popular NLP applications, machine translation (MT). Wrong pronoun …
    @@ -639,10 +639,10 @@

    A Cross-Lingual Study of Homotransphobia on Twitter

    +

    What about ''em''? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns

    - We present a cross-lingual study of homotransphobia on Twitter, examining the prevalence and forms of homotransphobic content in tweets related to LGBT issues in seven languages. Our findings reveal that homotransphobia is a global problem that takes … + As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. Exclusion is particularly harmful in one of the most popular NLP applications, machine translation (MT). Wrong pronoun …
    @@ -651,10 +651,10 @@

    A Cross-Li
    -

    Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale

    +

    Leveraging Social Interactions to Detect Misinformation on Social Media

    - Machine learning models are now able to convert user-written text descriptions into naturalistic images. These models are available to anyone online and are being used to generate millions of images a day. We investigate these models and find that … + Detecting misinformation threads is crucial to guarantee a healthy environment on social media. We address the problem using the data set created during the COVID-19 pandemic. It contains cascades of tweets discussing information weakly labeled as …
    @@ -663,10 +663,10 @@

    Easily Accessible Te
    -

    ferret: a Framework for Benchmarking Explainers on Transformers

    +

    A Cross-Lingual Study of Homotransphobia on Twitter

    - As Transformers are increasingly relied upon to solve complex NLP problems, there is an increased need for their decisions to be humanly interpretable. While several explainable AI (XAI) techniques for interpreting the outputs of transformer-based … + We present a cross-lingual study of homotransphobia on Twitter, examining the prevalence and forms of homotransphobic content in tweets related to LGBT issues in seven languages. Our findings reveal that homotransphobia is a global problem that takes …
    @@ -675,10 +675,10 @@

    ferret: a Frame
    -

    Know Your Audience: Do LLMs Adapt to Different Age and Education Levels?

    +

    Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale

    - Large language models (LLMs) offer a range of new possibilities, including adapting the text to different audiences and their reading needs. But how well do they adapt? We evaluate the readability of answers generated by four state-of-the-art LLMs … + Machine learning models are now able to convert user-written text descriptions into naturalistic images. These models are available to anyone online and are being used to generate millions of images a day. We investigate these models and find that …
    @@ -687,10 +687,10 @@

    Know Your Audienc
    -

    It's Not Just Hate: A Multi-Dimensional Perspective on Detecting Harmful Speech Online

    +

    ferret: a Framework for Benchmarking Explainers on Transformers

    - Well-annotated data is a prerequisite for good Natural Language Processing models. Too often, though, annotation decisions are governed by optimizing time or annotator agreement. We make a case for nuanced efforts in an interdisciplinary setting for … + As Transformers are increasingly relied upon to solve complex NLP problems, there is an increased need for their decisions to be humanly interpretable. While several explainable AI (XAI) techniques for interpreting the outputs of transformer-based …
    @@ -699,10 +699,10 @@

    It's Not Just Hate: A Multi-
    -

    Twitter-Demographer: A Flow-based Tool to Enrich Twitter Data

    +

    Know Your Audience: Do LLMs Adapt to Different Age and Education Levels?

    - Twitter data have become essential to Natural Language Processing (NLP) and social science research, driving various scientific discoveries in recent years. However, the textual data alone are often not enough to conduct studies: especially, social … + Large language models (LLMs) offer a range of new possibilities, including adapting the text to different audiences and their reading needs. But how well do they adapt? We evaluate the readability of answers generated by four state-of-the-art LLMs …
    @@ -711,10 +711,10 @@

    Twitter-Demographer: A Flo
    -

    Bridging Fairness and Environmental Sustainability in Natural Language Processing

    +

    It's Not Just Hate: A Multi-Dimensional Perspective on Detecting Harmful Speech Online

    - Fairness and environmental impact are important research directions for the sustainable development of artificial intelligence. However, while each topic is an active research area in natural language processing (NLP), there is a surprising lack of … + Well-annotated data is a prerequisite for good Natural Language Processing models. Too often, though, annotation decisions are governed by optimizing time or annotator agreement. We make a case for nuanced efforts in an interdisciplinary setting for …
    @@ -723,10 +723,10 @@

    SocioProbe: What, When, and Where Language Models Learn about Sociodemographics

    +

    Twitter-Demographer: A Flow-based Tool to Enrich Twitter Data

    - Pre-trained language models (PLMs) have outperformed other NLP models on a wide range of tasks. Opting for a more thorough understanding of their capabilities and inner workings, researchers have established the extend to which they capture … + Twitter data have become essential to Natural Language Processing (NLP) and social science research, driving various scientific discoveries in recent years. However, the textual data alone are often not enough to conduct studies: especially, social …
    diff --git a/publication_types/1/page/4/index.html b/publication_types/1/page/4/index.html index c2a87b98..54e1728c 100644 --- a/publication_types/1/page/4/index.html +++ b/publication_types/1/page/4/index.html @@ -271,7 +271,7 @@ - + @@ -615,10 +615,10 @@

    1

    -

    Measuring Harmful Representations in Scandinavian Language Models

    +

    Bridging Fairness and Environmental Sustainability in Natural Language Processing

    - Scandinavian countries are perceived as role-models when it comes to gender equality. With the advent of pre-trained language models and their widespread usage, we investigate to what extent gender-based harmful and toxic content exist in selected … + Fairness and environmental impact are important research directions for the sustainable development of artificial intelligence. However, while each topic is an active research area in natural language processing (NLP), there is a surprising lack of …
    @@ -627,10 +627,10 @@

    Mea
    -

    Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages

    +

    SocioProbe: What, When, and Where Language Models Learn about Sociodemographics

    - Hate speech is a global phenomenon, but most hate speech datasets so far focus on English-language content. This hinders the development of more effective hate speech detection models in hundreds of languages spoken by billions across the world. More … + Pre-trained language models (PLMs) have outperformed other NLP models on a wide range of tasks. Opting for a more thorough understanding of their capabilities and inner workings, researchers have established the extend to which they capture …
    @@ -639,10 +639,10 @@

    Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender

    +

    Measuring Harmful Representations in Scandinavian Language Models

    - The world of pronouns is changing – from a closed word class with few members to an open set of terms to reflect identities. However, Natural Language Processing (NLP) barely reflects this linguistic shift, resulting in the possible exclusion of … + Scandinavian countries are perceived as role-models when it comes to gender equality. With the advent of pre-trained language models and their widespread usage, we investigate to what extent gender-based harmful and toxic content exist in selected …
    @@ -651,10 +651,10 @@

    Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design

    +

    Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages

    - Over the last several years, end-to-end neural conversational agents have vastly improved their ability to carry unrestricted, open-domain conversations with humans. However, these models are often trained on large datasets from the Internet and, as … + Hate speech is a global phenomenon, but most hate speech datasets so far focus on English-language content. This hinders the development of more effective hate speech detection models in hundreds of languages spoken by billions across the world. More …
    @@ -663,10 +663,10 @@

    HATE-ITA: Hate Speech Detection in Italian Social Media Text

    +

    Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender

    - Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted properly. While Natural Language Processing supplies appropriate algorithms for trying to reach this objective, all research efforts are directed toward the … + The world of pronouns is changing – from a closed word class with few members to an open set of terms to reflect identities. However, Natural Language Processing (NLP) barely reflects this linguistic shift, resulting in the possible exclusion of …
    @@ -675,10 +675,10 @@

    HAT
    -

    Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models

    +

    Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design

    - Hate speech detection models are typically evaluated on held-out test sets. However, this risks painting an incomplete and potentially misleading picture of model performance because of increasingly well-documented systematic gaps and biases in hate … + Over the last several years, end-to-end neural conversational agents have vastly improved their ability to carry unrestricted, open-domain conversations with humans. However, these models are often trained on large datasets from the Internet and, as …
    @@ -687,10 +687,10 @@

    Hard and Soft Evaluation of NLP models with BOOtSTrap SAmpling - BooStSa

    +

    HATE-ITA: Hate Speech Detection in Italian Social Media Text

    - Natural Language Processing (NLP) ‘s applied nature makes it necessary to select the most effective and robust models. Producing slightly higher performance is insufficient; we want to know whether this advantage will carry over to other data sets. … + Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted properly. While Natural Language Processing supplies appropriate algorithms for trying to reach this objective, all research efforts are directed toward the …
    @@ -699,10 +699,10 @@

    MilaNLP at SemEval-2022 Task 5: Using Perceiver IO for Detecting Misogynous Memes with Text and Image Modalities

    +

    Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models

    - In this paper, we describe the system proposed by the MilaNLP team for the Multimedia Automatic Misogyny Identification (MAMI) challenge. We use Perceiver IO as a multimodal late fusion over unimodal streams to address both sub-tasks A and B. We … + Hate speech detection models are typically evaluated on held-out test sets. However, this risks painting an incomplete and potentially misleading picture of model performance because of increasingly well-documented systematic gaps and biases in hate …
    @@ -711,10 +711,10 @@

    Language Invariant Properties in Natural Language Processing

    +

    Hard and Soft Evaluation of NLP models with BOOtSTrap SAmpling - BooStSa

    - Meaning is context-dependent, but many properties of language (should) remain the same even if we transform the context. For example, sentiment, entailment, or speaker properties should be the same in a translation and original of a text. We … + Natural Language Processing (NLP) ‘s applied nature makes it necessary to select the most effective and robust models. Producing slightly higher performance is insufficient; we want to know whether this advantage will carry over to other data sets. …
    @@ -723,10 +723,10 @@

    Language Inv
    -

    Benchmarking Post-Hoc Interpretability Approaches for Transformer-based Misogyny Detection

    +

    MilaNLP at SemEval-2022 Task 5: Using Perceiver IO for Detecting Misogynous Memes with Text and Image Modalities

    - Transformer-based Natural Language Processing models have become the standard for hate speech detection. However, the unconscious use of these techniques for such a critical task comes with negative consequences. Various works have demonstrated that … + In this paper, we describe the system proposed by the MilaNLP team for the Multimedia Automatic Misogyny Identification (MAMI) challenge. We use Perceiver IO as a multimodal late fusion over unimodal streams to address both sub-tasks A and B. We …
    diff --git a/publication_types/1/page/5/index.html b/publication_types/1/page/5/index.html index 7b00a19d..180243fd 100644 --- a/publication_types/1/page/5/index.html +++ b/publication_types/1/page/5/index.html @@ -271,7 +271,7 @@ - + @@ -615,10 +615,10 @@

    1

    -

    Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals

    +

    Language Invariant Properties in Natural Language Processing

    - Current language technology is ubiquitous and directly influences individuals' lives worldwide. Given the recent trend in AI on training and constantly releasing new and powerful large language models (LLMs), there is a need to assess their biases … + Meaning is context-dependent, but many properties of language (should) remain the same even if we transform the context. For example, sentiment, entailment, or speaker properties should be the same in a translation and original of a text. We …
    @@ -627,10 +627,10 @@

    Mea
    -

    Pipelines for Social Bias Testing of Large Language Models

    +

    Benchmarking Post-Hoc Interpretability Approaches for Transformer-based Misogyny Detection

    - The maturity level of language models is now at a stage in which many companies rely on them to solve various tasks. However, while research has shown how biased and harmful these models are, **systematic ways of integrating social bias tests into … + Transformer-based Natural Language Processing models have become the standard for hate speech detection. However, the unconscious use of these techniques for such a critical task comes with negative consequences. Various works have demonstrated that …
    @@ -639,10 +639,10 @@

    -

    Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks

    +

    Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals

    - Labelled data is the foundation of most natural language processing tasks. However, labelling data is difficult and there often are diverse valid beliefs about what the correct data labels should be. So far, dataset creators have acknowledged … + Current language technology is ubiquitous and directly influences individuals' lives worldwide. Given the recent trend in AI on training and constantly releasing new and powerful large language models (LLMs), there is a need to assess their biases …
    @@ -651,10 +651,10 @@

    XLM-EMO: Multilingual Emotion Prediction in Social Media Text

    +

    Pipelines for Social Bias Testing of Large Language Models

    - Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the … + The maturity level of language models is now at a stage in which many companies rely on them to solve various tasks. However, while research has shown how biased and harmful these models are, **systematic ways of integrating social bias tests into …
    @@ -663,10 +663,10 @@

    XLM-EMO
    -

    DS-TOD: Efficient Domain Specialization for Task Oriented Dialog

    +

    Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks

    - Recent work has shown that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over traditional language modeling (LM) pretraining in downstream task-oriented dialog (TOD). These approaches, however, … + Labelled data is the foundation of most natural language processing tasks. However, labelling data is difficult and there often are diverse valid beliefs about what the correct data labels should be. So far, dataset creators have acknowledged …
    @@ -675,10 +675,10 @@

    DS-TOD: Efficient Domain Sp
    -

    Fair and Argumentative Language Modeling for Computational Argumentation

    +

    XLM-EMO: Multilingual Emotion Prediction in Social Media Text

    - Although much work in NLP has focused on measuring and mitigating stereotypical bias in semantic spaces, research addressing bias in computational argumentation is still in its infancy. In this paper, we address this research gap and conduct a … + Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the …
    @@ -687,10 +687,10 @@

    Fair and Argumentative Langu
    -

    Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists

    +

    DS-TOD: Efficient Domain Specialization for Task Oriented Dialog

    - Natural Language Processing (NLP) models risk overfitting to specific terms in the training data, thereby reducing their performance, fairness, and generalizability. E.g., neural hate speech detection models are strongly influenced by identity terms … + Recent work has shown that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over traditional language modeling (LM) pretraining in downstream task-oriented dialog (TOD). These approaches, however, …
    @@ -699,10 +699,10 @@

    Entropy-
    -

    SAFETYKIT: First Aid for Measuring Safety in Open-domain Conversational Systems

    +

    Fair and Argumentative Language Modeling for Computational Argumentation

    - The social impact of natural language processing and its applications has received increasing attention. In this position paper, we focus on the problem of safety for end-to-end conversational AI. We survey the problem landscape therein, introducing … + Although much work in NLP has focused on measuring and mitigating stereotypical bias in semantic spaces, research addressing bias in computational argumentation is still in its infancy. In this paper, we address this research gap and conduct a …
    @@ -711,10 +711,10 @@

    On the Gap between Adoption and Understanding in NLP

    +

    Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists

    - There are some issues with current research trends in NLP that can hamper the free development of scientific research. We identify five of particular concern: 1) the early adoption of methods without sufficient understanding or analysis; 2) the … + Natural Language Processing (NLP) models risk overfitting to specific terms in the training data, thereby reducing their performance, fairness, and generalizability. E.g., neural hate speech detection models are strongly influenced by identity terms …
    @@ -723,10 +723,10 @@

    On the Gap
    -

    Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence

    +

    SAFETYKIT: First Aid for Measuring Safety in Open-domain Conversational Systems

    - Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data. However, the resulting word groups are often not coherent, making them harder to interpret. Recently, neural … + The social impact of natural language processing and its applications has received increasing attention. In this position paper, we focus on the problem of safety for end-to-end conversational AI. We survey the problem landscape therein, introducing …
    diff --git a/publication_types/1/page/6/index.html b/publication_types/1/page/6/index.html index a0a293ef..7c68ffcc 100644 --- a/publication_types/1/page/6/index.html +++ b/publication_types/1/page/6/index.html @@ -271,7 +271,7 @@ - + @@ -615,10 +615,10 @@

    1

    -

    Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection

    +

    On the Gap between Adoption and Understanding in NLP

    - Reducing and counter-acting hate speech on Social Media is a significant concern. Most of the proposed automatic methods are conducted exclusively on English and very few consistently labeled, non-English resources have been proposed. Learning to … + There are some issues with current research trends in NLP that can hamper the free development of scientific research. We identify five of particular concern: 1) the early adoption of methods without sufficient understanding or analysis; 2) the …
    @@ -627,10 +627,10 @@

    Exposing the
    -

    'We will Reduce Taxes' - Identifying Election Pledges with Language Models

    +

    Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence

    - In an election campaign, political parties pledge to implement various projects--should they be elected. But do they follow through? To track election pledges from parties' election manifestos, we need to distinguish between pledges and general … + Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data. However, the resulting word groups are often not coherent, making them harder to interpret. Recently, neural …
    @@ -639,10 +639,10 @@

    'We will Reduce Taxes
    -

    HONEST: Measuring Hurtful Sentence Completion in Language Models

    +

    Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection

    - Language models have revolutionized the field of NLP. However, language models capture and proliferate hurtful stereotypes, especially in text generation. Our results show that **4.3% of the time, language models complete a sentence with a hurtful … + Reducing and counter-acting hate speech on Social Media is a significant concern. Most of the proposed automatic methods are conducted exclusively on English and very few consistently labeled, non-English resources have been proposed. Learning to …
    @@ -651,10 +651,10 @@

    HONEST: Measurin
    -

    The Importance of Modeling Social Factors of Language: Theory and Practice

    +

    'We will Reduce Taxes' - Identifying Election Pledges with Language Models

    - Natural language processing (NLP) applications are now more powerful and ubiquitous than ever before. With rapidly developing (neural) models and ever-more available data, current NLP models have access to more information than any human speaker … + In an election campaign, political parties pledge to implement various projects--should they be elected. But do they follow through? To track election pledges from parties' election manifestos, we need to distinguish between pledges and general …
    @@ -663,10 +663,10 @@

    Th
    -

    Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction

    +

    HONEST: Measuring Hurtful Sentence Completion in Language Models

    - We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines. + Language models have revolutionized the field of NLP. However, language models capture and proliferate hurtful stereotypes, especially in text generation. Our results show that **4.3% of the time, language models complete a sentence with a hurtful …
    @@ -675,10 +675,10 @@

    Language in a (Search
    -

    FEEL-IT: Emotion and Sentiment Classification for the Italian Language

    +

    The Importance of Modeling Social Factors of Language: Theory and Practice

    - Sentiment analysis is a common task to understand people's reactions online. Still, we often need more nuanced information: is the post negative because the user is angry or because they are sad? An abundance of approaches has been introduced for … + Natural language processing (NLP) applications are now more powerful and ubiquitous than ever before. With rapidly developing (neural) models and ever-more available data, current NLP models have access to more information than any human speaker …
    @@ -687,10 +687,10 @@

    FEEL-IT: Emot
    -

    MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?

    +

    Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction

    - The paper describes the MilaNLP team’s submission (Bocconi University, Milan) in the WASSA 2021 Shared Task on Empathy Detection and Emotion Classification. We focus on Track 2 - Emotion Classification - which consists of predicting the emotion of … + We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines.
    @@ -699,10 +699,10 @@

    MilaNLP @ WASSA: Does
    -

    Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning

    +

    FEEL-IT: Emotion and Sentiment Classification for the Italian Language

    - Supervised learning assumes that a ground truth label exists. However, the reliability of this ground truth depends on human annotators, who often disagree. Prior work has shown that this disagreement can be helpful in training models. We propose a … + Sentiment analysis is a common task to understand people's reactions online. Still, we often need more nuanced information: is the post negative because the user is angry or because they are sad? An abundance of approaches has been introduced for …
    @@ -711,10 +711,10 @@

    Beyond Black & White: Lev
    -

    Universal Joy A Data Set and Results for Classifying Emotions Across Languages

    +

    MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?

    - While emotions are universal aspects of human psychology, they are expressed differently across different languages and cultures. We introduce a new data set of over 530k anonymized public Facebook posts across 18 languages, labeled with five … + The paper describes the MilaNLP team’s submission (Bocconi University, Milan) in the WASSA 2021 Shared Task on Empathy Detection and Emotion Classification. We focus on Track 2 - Emotion Classification - which consists of predicting the emotion of …
    @@ -723,10 +723,10 @@

    Universal Joy A Data Set and Res
    -

    BERTective: Language Models and Contextual Information for Deception Detection

    +

    Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning

    - Spotting a lie is challenging but has an enormous potential impact on security as well as private and public safety. Several NLP methods have been proposed to classify texts as truthful or deceptive. In most cases, however, the target texts’ … + Supervised learning assumes that a ground truth label exists. However, the reliability of this ground truth depends on human annotators, who often disagree. Prior work has shown that this disagreement can be helpful in training models. We propose a …
    diff --git a/publication_types/1/page/7/index.html b/publication_types/1/page/7/index.html index 1f219778..88010256 100644 --- a/publication_types/1/page/7/index.html +++ b/publication_types/1/page/7/index.html @@ -271,7 +271,7 @@ - + @@ -615,10 +615,10 @@

    1

    -

    Cross-lingual Contextualized Topic Models with Zero-shot Learning

    +

    Universal Joy A Data Set and Results for Classifying Emotions Across Languages

    - We introduce a novel topic modeling method that can make use of contextulized embeddings (e.g., BERT) to do zero-shot cross-lingual topic modeling. + While emotions are universal aspects of human psychology, they are expressed differently across different languages and cultures. We introduce a new data set of over 530k anonymized public Facebook posts across 18 languages, labeled with five …
    @@ -627,10 +627,10 @@

    Cross-lingual Context
    -

    Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview

    +

    BERTective: Language Models and Contextual Information for Deception Detection

    - An increasing number of natural language processing papers address the effect of bias on predictions, introducing mitigation techniques at different parts of the standard NLP pipeline (data and models). However, these works have been conducted … + Spotting a lie is challenging but has an enormous potential impact on security as well as private and public safety. Several NLP methods have been proposed to classify texts as truthful or deceptive. In most cases, however, the target texts’ …
    @@ -639,10 +639,10 @@

    Predictive Biases in Natural Language Pro
    -

    “You Sound Just Like Your Father” Commercial Machine Translation Systems Include Stylistic Biases

    +

    Cross-lingual Contextualized Topic Models with Zero-shot Learning

    - The main goal of machine translation has been to convey the correct content. Stylistic considerations have been at best secondary. We show that as a consequence, the output of three commercial machine translation systems (Bing, DeepL, Google) make … + We introduce a novel topic modeling method that can make use of contextulized embeddings (e.g., BERT) to do zero-shot cross-lingual topic modeling.
    @@ -651,10 +651,10 @@

    “You Sound Just Like Your Father” Comme
    -

    Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success

    +

    Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview

    - When interacting with each other, we motivate, advise, inform, show love or power towards our peers. However, the way we interact may also hold some indication on how successful we are, as people often try to help each other to achieve their goals. … + An increasing number of natural language processing papers address the effect of bias on predictions, introducing mitigation techniques at different parts of the standard NLP pipeline (data and models). However, these works have been conducted …
    @@ -663,10 +663,10 @@

    Helpful or Hierarchical? Predicting th
    -

    A Case for Soft Loss Functions

    +

    “You Sound Just Like Your Father” Commercial Machine Translation Systems Include Stylistic Biases

    - Recently, Peterson et al. provided evidence of the benefits of using probabilistic soft labels generated from crowd annotations for training a computer vision model, showing that using such labels maximizes performance of the models over unseen data. … + The main goal of machine translation has been to convey the correct content. Stylistic considerations have been at best secondary. We show that as a consequence, the output of three commercial machine translation systems (Bing, DeepL, Google) make …
    @@ -675,10 +675,10 @@

    A Case for Soft Loss Functions
    -

    Dense Node Representation for Geolocation

    +

    Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success

    - Prior research has shown that geolocation can be substantially improved by including user network information. While effective, it suffers from the curse of dimensionality, since networks are usually represented as sparse adjacency matrices of … + When interacting with each other, we motivate, advise, inform, show love or power towards our peers. However, the way we interact may also hold some indication on how successful we are, as people often try to help each other to achieve their goals. …
    @@ -687,10 +687,10 @@

    Dense Node Representation for Geolocation<
    -

    Geolocation with Attention-Based Multitask Learning Models

    +

    A Case for Soft Loss Functions

    - Geolocation, predicting the location of a post based on text and other information, has a huge potential for several social media applications. Typically, the problem is modeled as either multi-class classification or regression. In the first case, … + Recently, Peterson et al. provided evidence of the benefits of using probabilistic soft labels generated from crowd annotations for training a computer vision model, showing that using such labels maximizes performance of the models over unseen data. …
    @@ -699,10 +699,10 @@

    Geolocation with Attention-Based Multi
    -

    Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers

    +

    Dense Node Representation for Geolocation

    - User reviews provide a significant source of information for companies to understand their market and audience. In order to discover broad trends in this source, researchers have typically used topic models such as Latent Dirichlet Allocation (LDA). … + Prior research has shown that geolocation can be substantially improved by including user network information. While effective, it suffers from the curse of dimensionality, since networks are usually represented as sparse adjacency matrices of …
    @@ -711,10 +711,10 @@

    Hey Siri. Ok Google. Alexa: A topic model
    -

    Identifying Linguistic Areas for Geolocation

    +

    Geolocation with Attention-Based Multitask Learning Models

    - Geolocating social media posts relies on the assumption that language carries sufficient geographic information. However, locations are usually given as continuous latitude/longitude tuples, so we first need to define discrete geographic regions … + Geolocation, predicting the location of a post based on text and other information, has a huge potential for several social media applications. Typically, the problem is modeled as either multi-class classification or regression. In the first case, …
    @@ -723,10 +723,10 @@

    Identifying Linguistic Areas for Geolocati
    -

    Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing

    +

    Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers

    - Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have still not adapted to account for … + User reviews provide a significant source of information for companies to understand their market and audience. In order to discover broad trends in this source, researchers have typically used topic models such as Latent Dirichlet Allocation (LDA). …
    diff --git a/publication_types/1/page/8/index.html b/publication_types/1/page/8/index.html index 1f76bf6b..8b1438c7 100644 --- a/publication_types/1/page/8/index.html +++ b/publication_types/1/page/8/index.html @@ -271,7 +271,7 @@ - + @@ -614,6 +614,30 @@

    1

    +
    +

    Identifying Linguistic Areas for Geolocation

    +
    + + Geolocating social media posts relies on the assumption that language carries sufficient geographic information. However, locations are usually given as continuous latitude/longitude tuples, so we first need to define discrete geographic regions … + +
    +
    + + + + +
    +

    Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing

    +
    + + Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have still not adapted to account for … + +
    +
    + + + +

    Increasing In-Class Similarity by Retrofitting Embeddings with Demographic Information

    diff --git a/publication_types/index.html b/publication_types/index.html index 0646e501..1560b79d 100644 --- a/publication_types/index.html +++ b/publication_types/index.html @@ -271,7 +271,7 @@ - + diff --git a/publication_types/index.xml b/publication_types/index.xml index 073885bb..1dc70a9e 100644 --- a/publication_types/index.xml +++ b/publication_types/index.xml @@ -5,7 +5,7 @@ https://milanlproc.github.io/publication_types/ Publication_types - Source Themes Academic (https://sourcethemes.com/academic/)en-us© MilaNLP, 2024Sun, 22 Sep 2024 00:00:00 +0000 + Source Themes Academic (https://sourcethemes.com/academic/)en-us© MilaNLP, 2024Tue, 03 Dec 2024 00:00:00 +0000 img/map[gravatar:%!s(bool=false) shape:circle] Publication_types diff --git a/sitemap.xml b/sitemap.xml index 1be08641..a70e50ca 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -3,16 +3,76 @@ xmlns:xhtml="http://www.w3.org/1999/xhtml"> https://milanlproc.github.io/publication_types/1/ - 2024-09-22T00:00:00+00:00 + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/authors/adina-williams/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/tags/ai-alignment/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/authors/alexander-whitefield/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/authors/andrew-m.-bean/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/authors/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/authors/bertie-vidgen/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/authors/hannah-rose-kirk/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/authors/he-he/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/authors/juan-manuel-ciro/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/authors/katerina-margatina/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/tags/large-language-models/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/authors/max-bartolo/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/tags/nlp/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/authors/paul-rottger/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/publication_types/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/publication/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/authors/rafael-mosquera/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/authors/scott-a.-hale/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/tags/ + 2024-12-03T00:00:00+00:00 + + https://milanlproc.github.io/publication/2024-prism/ + 2024-12-03T00:00:00+00:00 https://milanlproc.github.io/authors/alba-curry/ 2024-09-22T00:00:00+00:00 https://milanlproc.github.io/authors/amanda-cercas-curry/ 2024-09-22T00:00:00+00:00 - - https://milanlproc.github.io/authors/ - 2024-09-22T00:00:00+00:00 https://milanlproc.github.io/tags/bias/ 2024-09-22T00:00:00+00:00 @@ -28,18 +88,6 @@ https://milanlproc.github.io/authors/flor-miriam-plaza-del-arco/ 2024-09-22T00:00:00+00:00 - - https://milanlproc.github.io/tags/large-language-models/ - 2024-09-22T00:00:00+00:00 - - https://milanlproc.github.io/ - 2024-09-22T00:00:00+00:00 - - https://milanlproc.github.io/publication_types/ - 2024-09-22T00:00:00+00:00 - - https://milanlproc.github.io/publication/ - 2024-09-22T00:00:00+00:00 https://milanlproc.github.io/tags/religion/ 2024-09-22T00:00:00+00:00 @@ -49,12 +97,6 @@ https://milanlproc.github.io/authors/susanna-paoli/ 2024-09-22T00:00:00+00:00 - - https://milanlproc.github.io/tags/ - 2024-09-22T00:00:00+00:00 - - https://milanlproc.github.io/tags/ai-alignment/ - 2024-08-11T00:00:00+00:00 https://milanlproc.github.io/tags/ai-safety/ 2024-08-11T00:00:00+00:00 @@ -79,9 +121,6 @@ https://milanlproc.github.io/authors/frauke-kreuter/ 2024-08-11T00:00:00+00:00 - - https://milanlproc.github.io/authors/hannah-rose-kirk/ - 2024-08-11T00:00:00+00:00 https://milanlproc.github.io/authors/hinrich-schuetze/ 2024-08-11T00:00:00+00:00 @@ -94,12 +133,6 @@ https://milanlproc.github.io/publication/2024-myanswerisc/ 2024-08-11T00:00:00+00:00 - - https://milanlproc.github.io/tags/nlp/ - 2024-08-11T00:00:00+00:00 - - https://milanlproc.github.io/authors/paul-rottger/ - 2024-08-11T00:00:00+00:00 https://milanlproc.github.io/publication/2024-politicalcompass/ 2024-08-11T00:00:00+00:00 @@ -113,8 +146,26 @@ https://milanlproc.github.io/authors/xinpeng-wang/ 2024-08-11T00:00:00+00:00 - https://milanlproc.github.io/authors/bertie-vidgen/ - 2024-07-16T00:00:00+00:00 + https://milanlproc.github.io/authors/diyi-liu/ + 2024-07-20T00:00:00+00:00 + + https://milanlproc.github.io/publication/2024-hate-geographies/ + 2024-07-20T00:00:00+00:00 + + https://milanlproc.github.io/tags/hate-speech-detection/ + 2024-07-20T00:00:00+00:00 + + https://milanlproc.github.io/authors/manuel-tonneau/ + 2024-07-20T00:00:00+00:00 + + https://milanlproc.github.io/tags/online-hate/ + 2024-07-20T00:00:00+00:00 + + https://milanlproc.github.io/authors/ralph-schroeder/ + 2024-07-20T00:00:00+00:00 + + https://milanlproc.github.io/authors/samuel-fraiberger/ + 2024-07-20T00:00:00+00:00 https://milanlproc.github.io/authors/federico-bianchi/ 2024-07-16T00:00:00+00:00 @@ -289,9 +340,6 @@ https://milanlproc.github.io/tags/alignment/ 2023-11-15T00:00:00+00:00 - - https://milanlproc.github.io/authors/scott-a.-hale/ - 2023-11-15T00:00:00+00:00 https://milanlproc.github.io/publication/2023-alignmentparadigms/ 2023-11-15T00:00:00+00:00 @@ -313,9 +361,6 @@ https://milanlproc.github.io/open_positions/visiting_researcher/ 2023-10-23T00:00:00+00:00 - - https://milanlproc.github.io/authors/andrew-m.-bean/ - 2023-10-11T00:00:00+00:00 https://milanlproc.github.io/tags/human-feedback/ 2023-10-11T00:00:00+00:00 diff --git a/tags/ai-alignment/index.html b/tags/ai-alignment/index.html index 40e0d332..0b0f3709 100644 --- a/tags/ai-alignment/index.html +++ b/tags/ai-alignment/index.html @@ -271,7 +271,7 @@ - + @@ -614,6 +614,18 @@

    AI Alignment

    +
    +

    The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models

    +
    + + Human feedback is central to the alignment of Large Language Models (LLMs). However, open questions remain about methods (how), domains (where), people (who) and objectives (to what end) of feedback processes. To navigate these questions, we … + +
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    + + + +

    Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models

    diff --git a/tags/ai-alignment/index.xml b/tags/ai-alignment/index.xml index 9cd17f9f..941b31ab 100644 --- a/tags/ai-alignment/index.xml +++ b/tags/ai-alignment/index.xml @@ -5,13 +5,21 @@ https://milanlproc.github.io/tags/ai-alignment/ AI Alignment - Source Themes Academic (https://sourcethemes.com/academic/)en-us© MilaNLP, 2024Sun, 11 Aug 2024 00:00:00 +0000 + Source Themes Academic (https://sourcethemes.com/academic/)en-us© MilaNLP, 2024Tue, 03 Dec 2024 00:00:00 +0000 img/map[gravatar:%!s(bool=false) shape:circle] AI Alignment https://milanlproc.github.io/tags/ai-alignment/ + + The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models + https://milanlproc.github.io/publication/2024-prism/ + Tue, 03 Dec 2024 00:00:00 +0000 + https://milanlproc.github.io/publication/2024-prism/ + + + Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models https://milanlproc.github.io/publication/2024-politicalcompass/ diff --git a/tags/hate-speech-detection/index.html b/tags/hate-speech-detection/index.html new file mode 100644 index 00000000..134625cc --- /dev/null +++ b/tags/hate-speech-detection/index.html @@ -0,0 +1,792 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Hate Speech Detection | MilaNLP Lab @ Bocconi University + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +

    Hate Speech Detection

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    + + + + + + + +
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    From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets

    +
    + + Perceptions of hate can vary greatly across cultural contexts. Hate speech (HS) datasets, however, have traditionally been developed by language. This hides potential cultural biases, as one language may be spoken in different countries home to … + +
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    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + + + + + + + diff --git a/tags/hate-speech-detection/index.xml b/tags/hate-speech-detection/index.xml new file mode 100644 index 00000000..ae024a53 --- /dev/null +++ b/tags/hate-speech-detection/index.xml @@ -0,0 +1,24 @@ + + + + Hate Speech Detection | MilaNLP Lab @ Bocconi University + https://milanlproc.github.io/tags/hate-speech-detection/ + + Hate Speech Detection + Source Themes Academic (https://sourcethemes.com/academic/)en-us© MilaNLP, 2024Sat, 20 Jul 2024 00:00:00 +0000 + + img/map[gravatar:%!s(bool=false) shape:circle] + Hate Speech Detection + https://milanlproc.github.io/tags/hate-speech-detection/ + + + + From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets + https://milanlproc.github.io/publication/2024-hate-geographies/ + Sat, 20 Jul 2024 00:00:00 +0000 + https://milanlproc.github.io/publication/2024-hate-geographies/ + + + + + diff --git a/tags/hate-speech-detection/page/1/index.html b/tags/hate-speech-detection/page/1/index.html new file mode 100644 index 00000000..6860c1ed --- /dev/null +++ b/tags/hate-speech-detection/page/1/index.html @@ -0,0 +1 @@ +https://milanlproc.github.io/tags/hate-speech-detection/ \ No newline at end of file diff --git a/tags/index.html b/tags/index.html index 7e28efa7..fc1fd017 100644 --- a/tags/index.html +++ b/tags/index.html @@ -271,7 +271,7 @@ - + @@ -615,7 +615,7 @@

    Tags

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    AI Alignment

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    Emotion attribution

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    Large Language Models

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    AI Safety

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    demographic prediction

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    Hate Speech Detection

    diff --git a/tags/index.xml b/tags/index.xml index f933c9d6..53aef119 100644 --- a/tags/index.xml +++ b/tags/index.xml @@ -5,7 +5,7 @@ https://milanlproc.github.io/tags/ Tags - Source Themes Academic (https://sourcethemes.com/academic/)en-us© MilaNLP, 2024Sun, 22 Sep 2024 00:00:00 +0000 + Source Themes Academic (https://sourcethemes.com/academic/)en-us© MilaNLP, 2024Tue, 03 Dec 2024 00:00:00 +0000 img/map[gravatar:%!s(bool=false) shape:circle] Tags diff --git a/tags/large-language-models/index.html b/tags/large-language-models/index.html index 3294cb0f..864545d4 100644 --- a/tags/large-language-models/index.html +++ b/tags/large-language-models/index.html @@ -271,7 +271,7 @@ - + @@ -614,6 +614,18 @@

    Large Language Models

    +
    +

    The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models

    +
    + + Human feedback is central to the alignment of Large Language Models (LLMs). However, open questions remain about methods (how), domains (where), people (who) and objectives (to what end) of feedback processes. To navigate these questions, we … + +
    +
    + + + + - - - -
    -

    Classist Tools: Social Class Correlates with Performance in NLP

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    - - Since the foundational work of William Labov on the social stratification of language (Labov, 1964), linguistics has made concentrated efforts to explore the links between sociodemographic characteristics and language production and perception. But … - -
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    The State of Pr
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    What about ''em''? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns

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    The Ecological Fallacy in Annotation: Modeling Human Label Variation goes beyond Sociodemographics

    - As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. Exclusion is particularly harmful in one of the most popular NLP applications, machine translation (MT). Wrong pronoun … + Many NLP tasks exhibit human label variation, where different annotators give different labels to the same texts. This variation is known to depend, at least in part, on the sociodemographics of annotators. Recent research aims to model individual …
    @@ -723,10 +723,10 @@

    -

    What about ''em''? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns

    +

    The State of Profanity Obfuscation in Natural Language Processing Scientific Publications

    - As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. Exclusion is particularly harmful in one of the most popular NLP applications, machine translation (MT). Wrong pronoun … + Work on hate speech has made considering rude and harmful examples in scientific publications inevitable. This situation raises various problems, such as whether or not to obscure profanities. While science must accurately disclose what it does, the …
    diff --git a/tags/nlp/page/3/index.html b/tags/nlp/page/3/index.html index cb5bc5cf..3e361c5d 100644 --- a/tags/nlp/page/3/index.html +++ b/tags/nlp/page/3/index.html @@ -271,7 +271,7 @@ - + @@ -615,10 +615,10 @@

    NLP

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    Leveraging Social Interactions to Detect Misinformation on Social Media

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    What about ''em''? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns

    - Detecting misinformation threads is crucial to guarantee a healthy environment on social media. We address the problem using the data set created during the COVID-19 pandemic. It contains cascades of tweets discussing information weakly labeled as … + As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. Exclusion is particularly harmful in one of the most popular NLP applications, machine translation (MT). Wrong pronoun …
    @@ -627,10 +627,10 @@

    A Cross-Lingual Study of Homotransphobia on Twitter

    +

    What about ''em''? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns

    - We present a cross-lingual study of homotransphobia on Twitter, examining the prevalence and forms of homotransphobic content in tweets related to LGBT issues in seven languages. Our findings reveal that homotransphobia is a global problem that takes … + As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. Exclusion is particularly harmful in one of the most popular NLP applications, machine translation (MT). Wrong pronoun …
    @@ -639,10 +639,10 @@

    A Cross-Li
    -

    Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale

    +

    Leveraging Social Interactions to Detect Misinformation on Social Media

    - Machine learning models are now able to convert user-written text descriptions into naturalistic images. These models are available to anyone online and are being used to generate millions of images a day. We investigate these models and find that … + Detecting misinformation threads is crucial to guarantee a healthy environment on social media. We address the problem using the data set created during the COVID-19 pandemic. It contains cascades of tweets discussing information weakly labeled as …
    @@ -651,10 +651,10 @@

    Easily Accessible Te
    -

    Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)

    +

    A Cross-Lingual Study of Homotransphobia on Twitter

    - Natural Language Processing has seen impressive gains in recent years. This research includes the demonstration by NLP models to have turned into useful technologies with improved capabilities, measured in terms of how well they match human behavior … + We present a cross-lingual study of homotransphobia on Twitter, examining the prevalence and forms of homotransphobic content in tweets related to LGBT issues in seven languages. Our findings reveal that homotransphobia is a global problem that takes …
    @@ -663,10 +663,10 @@

    Proceedings of the First Wor
    -

    Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers

    +

    Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale

    - Demographic factors (e.g., gender or age) shape our language. Previous work showed that incorporating demographic factors can consistently improve performance for various NLP tasks with traditional NLP models. In this work, we investigate whether … + Machine learning models are now able to convert user-written text descriptions into naturalistic images. These models are available to anyone online and are being used to generate millions of images a day. We investigate these models and find that …
    @@ -675,10 +675,10 @@

    ferret: a Framework for Benchmarking Explainers on Transformers

    +

    Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)

    - As Transformers are increasingly relied upon to solve complex NLP problems, there is an increased need for their decisions to be humanly interpretable. While several explainable AI (XAI) techniques for interpreting the outputs of transformer-based … + Natural Language Processing has seen impressive gains in recent years. This research includes the demonstration by NLP models to have turned into useful technologies with improved capabilities, measured in terms of how well they match human behavior …
    @@ -687,10 +687,10 @@

    ferret: a Frame
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    INDOMITA

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    Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers

    - Innovative Demographically-aware Hate Speech Detection in Online Media in Italian + Demographic factors (e.g., gender or age) shape our language. Previous work showed that incorporating demographic factors can consistently improve performance for various NLP tasks with traditional NLP models. In this work, we investigate whether …
    @@ -699,10 +699,10 @@

    INDOMITA

    -

    Know Your Audience: Do LLMs Adapt to Different Age and Education Levels?

    +

    ferret: a Framework for Benchmarking Explainers on Transformers

    - Large language models (LLMs) offer a range of new possibilities, including adapting the text to different audiences and their reading needs. But how well do they adapt? We evaluate the readability of answers generated by four state-of-the-art LLMs … + As Transformers are increasingly relied upon to solve complex NLP problems, there is an increased need for their decisions to be humanly interpretable. While several explainable AI (XAI) techniques for interpreting the outputs of transformer-based …
    @@ -711,10 +711,10 @@

    Know Your Audienc
    -

    Beyond Digital 'Echo Chambers': The Role of Viewpoint Diversity in Political Discussion

    +

    INDOMITA

    - Increasingly taking place in online spaces, modern political conversations are typically perceived to be unproductively affirming---siloed in so called 'echo chambers' of exclusively like-minded discussants. Yet, to date we lack sufficient means to … + Innovative Demographically-aware Hate Speech Detection in Online Media in Italian
    @@ -723,10 +723,10 @@

    PERSONAE

    +

    Know Your Audience: Do LLMs Adapt to Different Age and Education Levels?

    - Personalized and Subjective approaches to Natural Language Processing + Large language models (LLMs) offer a range of new possibilities, including adapting the text to different audiences and their reading needs. But how well do they adapt? We evaluate the readability of answers generated by four state-of-the-art LLMs …
    diff --git a/tags/nlp/page/4/index.html b/tags/nlp/page/4/index.html index d8bb6fec..c2adc780 100644 --- a/tags/nlp/page/4/index.html +++ b/tags/nlp/page/4/index.html @@ -271,7 +271,7 @@ - + @@ -615,10 +615,10 @@

    NLP

    -

    It's Not Just Hate: A Multi-Dimensional Perspective on Detecting Harmful Speech Online

    +

    Beyond Digital 'Echo Chambers': The Role of Viewpoint Diversity in Political Discussion

    - Well-annotated data is a prerequisite for good Natural Language Processing models. Too often, though, annotation decisions are governed by optimizing time or annotator agreement. We make a case for nuanced efforts in an interdisciplinary setting for … + Increasingly taking place in online spaces, modern political conversations are typically perceived to be unproductively affirming---siloed in so called 'echo chambers' of exclusively like-minded discussants. Yet, to date we lack sufficient means to …
    @@ -627,10 +627,10 @@

    It's Not Just Hate: A Multi-
    -

    Twitter-Demographer: A Flow-based Tool to Enrich Twitter Data

    +

    PERSONAE

    - Twitter data have become essential to Natural Language Processing (NLP) and social science research, driving various scientific discoveries in recent years. However, the textual data alone are often not enough to conduct studies: especially, social … + Personalized and Subjective approaches to Natural Language Processing
    @@ -639,10 +639,10 @@

    Twitter-Demographer: A Flo
    -

    Bridging Fairness and Environmental Sustainability in Natural Language Processing

    +

    It's Not Just Hate: A Multi-Dimensional Perspective on Detecting Harmful Speech Online

    - Fairness and environmental impact are important research directions for the sustainable development of artificial intelligence. However, while each topic is an active research area in natural language processing (NLP), there is a surprising lack of … + Well-annotated data is a prerequisite for good Natural Language Processing models. Too often, though, annotation decisions are governed by optimizing time or annotator agreement. We make a case for nuanced efforts in an interdisciplinary setting for …
    @@ -651,10 +651,10 @@

    SocioProbe: What, When, and Where Language Models Learn about Sociodemographics

    +

    Twitter-Demographer: A Flow-based Tool to Enrich Twitter Data

    - Pre-trained language models (PLMs) have outperformed other NLP models on a wide range of tasks. Opting for a more thorough understanding of their capabilities and inner workings, researchers have established the extend to which they capture … + Twitter data have become essential to Natural Language Processing (NLP) and social science research, driving various scientific discoveries in recent years. However, the textual data alone are often not enough to conduct studies: especially, social …
    @@ -663,10 +663,10 @@

    Measuring Harmful Representations in Scandinavian Language Models

    +

    Bridging Fairness and Environmental Sustainability in Natural Language Processing

    - Scandinavian countries are perceived as role-models when it comes to gender equality. With the advent of pre-trained language models and their widespread usage, we investigate to what extent gender-based harmful and toxic content exist in selected … + Fairness and environmental impact are important research directions for the sustainable development of artificial intelligence. However, while each topic is an active research area in natural language processing (NLP), there is a surprising lack of …
    @@ -675,10 +675,10 @@

    Mea
    -

    Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages

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    SocioProbe: What, When, and Where Language Models Learn about Sociodemographics

    - Hate speech is a global phenomenon, but most hate speech datasets so far focus on English-language content. This hinders the development of more effective hate speech detection models in hundreds of languages spoken by billions across the world. More … + Pre-trained language models (PLMs) have outperformed other NLP models on a wide range of tasks. Opting for a more thorough understanding of their capabilities and inner workings, researchers have established the extend to which they capture …
    @@ -687,10 +687,10 @@

    Is It Worth the (Environmental) Cost? Limited Evidence for the Benefits of Diachronic Continuous Training

    +

    Measuring Harmful Representations in Scandinavian Language Models

    - Language is constantly changing and evolving, leaving language models to quickly become outdated, both factually and linguistically. Recent research proposes we continuously update our models using new data. Continuous training allows us to teach … + Scandinavian countries are perceived as role-models when it comes to gender equality. With the advent of pre-trained language models and their widespread usage, we investigate to what extent gender-based harmful and toxic content exist in selected …
    @@ -699,10 +699,10 @@

    Is I
    -

    Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender

    +

    Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages

    - The world of pronouns is changing – from a closed word class with few members to an open set of terms to reflect identities. However, Natural Language Processing (NLP) barely reflects this linguistic shift, resulting in the possible exclusion of … + Hate speech is a global phenomenon, but most hate speech datasets so far focus on English-language content. This hinders the development of more effective hate speech detection models in hundreds of languages spoken by billions across the world. More …
    @@ -711,10 +711,10 @@

    Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design

    +

    Is It Worth the (Environmental) Cost? Limited Evidence for the Benefits of Diachronic Continuous Training

    - Over the last several years, end-to-end neural conversational agents have vastly improved their ability to carry unrestricted, open-domain conversations with humans. However, these models are often trained on large datasets from the Internet and, as … + Language is constantly changing and evolving, leaving language models to quickly become outdated, both factually and linguistically. Recent research proposes we continuously update our models using new data. Continuous training allows us to teach …
    @@ -723,10 +723,10 @@

    HATE-ITA: Hate Speech Detection in Italian Social Media Text

    +

    Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender

    - Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted properly. While Natural Language Processing supplies appropriate algorithms for trying to reach this objective, all research efforts are directed toward the … + The world of pronouns is changing – from a closed word class with few members to an open set of terms to reflect identities. However, Natural Language Processing (NLP) barely reflects this linguistic shift, resulting in the possible exclusion of …
    diff --git a/tags/nlp/page/5/index.html b/tags/nlp/page/5/index.html index 0a7ef522..0aac0155 100644 --- a/tags/nlp/page/5/index.html +++ b/tags/nlp/page/5/index.html @@ -271,7 +271,7 @@ - + @@ -615,10 +615,10 @@

    NLP

    -

    Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models

    +

    Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design

    - Hate speech detection models are typically evaluated on held-out test sets. However, this risks painting an incomplete and potentially misleading picture of model performance because of increasingly well-documented systematic gaps and biases in hate … + Over the last several years, end-to-end neural conversational agents have vastly improved their ability to carry unrestricted, open-domain conversations with humans. However, these models are often trained on large datasets from the Internet and, as …
    @@ -627,10 +627,10 @@

    Hard and Soft Evaluation of NLP models with BOOtSTrap SAmpling - BooStSa

    +

    HATE-ITA: Hate Speech Detection in Italian Social Media Text

    - Natural Language Processing (NLP) ‘s applied nature makes it necessary to select the most effective and robust models. Producing slightly higher performance is insufficient; we want to know whether this advantage will carry over to other data sets. … + Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted properly. While Natural Language Processing supplies appropriate algorithms for trying to reach this objective, all research efforts are directed toward the …
    @@ -639,10 +639,10 @@

    Language Invariant Properties in Natural Language Processing

    +

    Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models

    - Meaning is context-dependent, but many properties of language (should) remain the same even if we transform the context. For example, sentiment, entailment, or speaker properties should be the same in a translation and original of a text. We … + Hate speech detection models are typically evaluated on held-out test sets. However, this risks painting an incomplete and potentially misleading picture of model performance because of increasingly well-documented systematic gaps and biases in hate …
    @@ -651,10 +651,10 @@

    Language Inv
    -

    MENTALISM

    +

    Hard and Soft Evaluation of NLP models with BOOtSTrap SAmpling - BooStSa

    - Measuring, Tracking, and Analyzing Inequality using Social Media + Natural Language Processing (NLP) ‘s applied nature makes it necessary to select the most effective and robust models. Producing slightly higher performance is insufficient; we want to know whether this advantage will carry over to other data sets. …
    @@ -663,10 +663,10 @@

    MENTALISM

    -

    Benchmarking Post-Hoc Interpretability Approaches for Transformer-based Misogyny Detection

    +

    Language Invariant Properties in Natural Language Processing

    - Transformer-based Natural Language Processing models have become the standard for hate speech detection. However, the unconscious use of these techniques for such a critical task comes with negative consequences. Various works have demonstrated that … + Meaning is context-dependent, but many properties of language (should) remain the same even if we transform the context. For example, sentiment, entailment, or speaker properties should be the same in a translation and original of a text. We …
    @@ -675,10 +675,10 @@

    -

    Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals

    +

    MENTALISM

    - Current language technology is ubiquitous and directly influences individuals' lives worldwide. Given the recent trend in AI on training and constantly releasing new and powerful large language models (LLMs), there is a need to assess their biases … + Measuring, Tracking, and Analyzing Inequality using Social Media
    @@ -687,10 +687,10 @@

    Mea
    -

    Pipelines for Social Bias Testing of Large Language Models

    +

    Benchmarking Post-Hoc Interpretability Approaches for Transformer-based Misogyny Detection

    - The maturity level of language models is now at a stage in which many companies rely on them to solve various tasks. However, while research has shown how biased and harmful these models are, **systematic ways of integrating social bias tests into … + Transformer-based Natural Language Processing models have become the standard for hate speech detection. However, the unconscious use of these techniques for such a critical task comes with negative consequences. Various works have demonstrated that …
    @@ -699,10 +699,10 @@

    -

    Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks

    +

    Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals

    - Labelled data is the foundation of most natural language processing tasks. However, labelling data is difficult and there often are diverse valid beliefs about what the correct data labels should be. So far, dataset creators have acknowledged … + Current language technology is ubiquitous and directly influences individuals' lives worldwide. Given the recent trend in AI on training and constantly releasing new and powerful large language models (LLMs), there is a need to assess their biases …
    @@ -711,10 +711,10 @@

    XLM-EMO: Multilingual Emotion Prediction in Social Media Text

    +

    Pipelines for Social Bias Testing of Large Language Models

    - Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the … + The maturity level of language models is now at a stage in which many companies rely on them to solve various tasks. However, while research has shown how biased and harmful these models are, **systematic ways of integrating social bias tests into …
    @@ -723,10 +723,10 @@

    XLM-EMO
    -

    DS-TOD: Efficient Domain Specialization for Task Oriented Dialog

    +

    Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks

    - Recent work has shown that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over traditional language modeling (LM) pretraining in downstream task-oriented dialog (TOD). These approaches, however, … + Labelled data is the foundation of most natural language processing tasks. However, labelling data is difficult and there often are diverse valid beliefs about what the correct data labels should be. So far, dataset creators have acknowledged …
    diff --git a/tags/nlp/page/6/index.html b/tags/nlp/page/6/index.html index 2aee09e2..06c1a232 100644 --- a/tags/nlp/page/6/index.html +++ b/tags/nlp/page/6/index.html @@ -271,7 +271,7 @@ - + @@ -615,10 +615,10 @@

    NLP

    -

    Fair and Argumentative Language Modeling for Computational Argumentation

    +

    XLM-EMO: Multilingual Emotion Prediction in Social Media Text

    - Although much work in NLP has focused on measuring and mitigating stereotypical bias in semantic spaces, research addressing bias in computational argumentation is still in its infancy. In this paper, we address this research gap and conduct a … + Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the …
    @@ -627,10 +627,10 @@

    Fair and Argumentative Langu
    -

    Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists

    +

    DS-TOD: Efficient Domain Specialization for Task Oriented Dialog

    - Natural Language Processing (NLP) models risk overfitting to specific terms in the training data, thereby reducing their performance, fairness, and generalizability. E.g., neural hate speech detection models are strongly influenced by identity terms … + Recent work has shown that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over traditional language modeling (LM) pretraining in downstream task-oriented dialog (TOD). These approaches, however, …
    @@ -639,10 +639,10 @@

    Entropy-
    -

    SAFETYKIT: First Aid for Measuring Safety in Open-domain Conversational Systems

    +

    Fair and Argumentative Language Modeling for Computational Argumentation

    - The social impact of natural language processing and its applications has received increasing attention. In this position paper, we focus on the problem of safety for end-to-end conversational AI. We survey the problem landscape therein, introducing … + Although much work in NLP has focused on measuring and mitigating stereotypical bias in semantic spaces, research addressing bias in computational argumentation is still in its infancy. In this paper, we address this research gap and conduct a …
    @@ -651,10 +651,10 @@

    Text Analysis in Python for Social Scientists – Prediction and Classification

    +

    Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists

    - Text contains a wealth of information about about a wide variety of sociocultural constructs. Automated prediction methods can infer these quantities (sentiment analysis is probably the most well-known application). However, there is virtually no … + Natural Language Processing (NLP) models risk overfitting to specific terms in the training data, thereby reducing their performance, fairness, and generalizability. E.g., neural hate speech detection models are strongly influenced by identity terms …
    @@ -663,10 +663,10 @@

    Text Analysis in Python for Social Scie
    -

    Learning from Disagreement: A Survey

    +

    SAFETYKIT: First Aid for Measuring Safety in Open-domain Conversational Systems

    - Many tasks in Natural Language Processing (NLP) and Computer Vision (CV) offer evidence that humans disagree, from objective tasks such as part-of-speech tagging to more subjective tasks such as classifying an image or deciding whether a proposition … + The social impact of natural language processing and its applications has received increasing attention. In this position paper, we focus on the problem of safety for end-to-end conversational AI. We survey the problem landscape therein, introducing …
    @@ -675,10 +675,10 @@

    Learning fro
    -

    Five sources of bias in natural language processing

    +

    Text Analysis in Python for Social Scientists – Prediction and Classification

    - Recently, there has been an increased interest in demographically grounded bias in natural language processing (NLP) applications. Much of the recent work has focused on describing bias and providing an overview of bias in a larger context. Here, we … + Text contains a wealth of information about about a wide variety of sociocultural constructs. Automated prediction methods can infer these quantities (sentiment analysis is probably the most well-known application). However, there is virtually no …
    @@ -687,10 +687,10 @@

    Five sources of bias in natu
    -

    On the Gap between Adoption and Understanding in NLP

    +

    Learning from Disagreement: A Survey

    - There are some issues with current research trends in NLP that can hamper the free development of scientific research. We identify five of particular concern: 1) the early adoption of methods without sufficient understanding or analysis; 2) the … + Many tasks in Natural Language Processing (NLP) and Computer Vision (CV) offer evidence that humans disagree, from objective tasks such as part-of-speech tagging to more subjective tasks such as classifying an image or deciding whether a proposition …
    @@ -699,10 +699,10 @@

    On the Gap
    -

    Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence

    +

    Five sources of bias in natural language processing

    - Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data. However, the resulting word groups are often not coherent, making them harder to interpret. Recently, neural … + Recently, there has been an increased interest in demographically grounded bias in natural language processing (NLP) applications. Much of the recent work has focused on describing bias and providing an overview of bias in a larger context. Here, we …
    @@ -711,10 +711,10 @@

    -

    Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection

    +

    On the Gap between Adoption and Understanding in NLP

    - Reducing and counter-acting hate speech on Social Media is a significant concern. Most of the proposed automatic methods are conducted exclusively on English and very few consistently labeled, non-English resources have been proposed. Learning to … + There are some issues with current research trends in NLP that can hamper the free development of scientific research. We identify five of particular concern: 1) the early adoption of methods without sufficient understanding or analysis; 2) the …
    @@ -723,10 +723,10 @@

    Exposing the
    -

    'We will Reduce Taxes' - Identifying Election Pledges with Language Models

    +

    Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence

    - In an election campaign, political parties pledge to implement various projects--should they be elected. But do they follow through? To track election pledges from parties' election manifestos, we need to distinguish between pledges and general … + Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data. However, the resulting word groups are often not coherent, making them harder to interpret. Recently, neural …
    diff --git a/tags/nlp/page/7/index.html b/tags/nlp/page/7/index.html index eb18819a..5f09c539 100644 --- a/tags/nlp/page/7/index.html +++ b/tags/nlp/page/7/index.html @@ -271,7 +271,7 @@ - + @@ -615,10 +615,10 @@

    NLP

    -

    HONEST: Measuring Hurtful Sentence Completion in Language Models

    +

    Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection

    - Language models have revolutionized the field of NLP. However, language models capture and proliferate hurtful stereotypes, especially in text generation. Our results show that **4.3% of the time, language models complete a sentence with a hurtful … + Reducing and counter-acting hate speech on Social Media is a significant concern. Most of the proposed automatic methods are conducted exclusively on English and very few consistently labeled, non-English resources have been proposed. Learning to …
    @@ -627,10 +627,10 @@

    HONEST: Measurin
    -

    The Importance of Modeling Social Factors of Language: Theory and Practice

    +

    'We will Reduce Taxes' - Identifying Election Pledges with Language Models

    - Natural language processing (NLP) applications are now more powerful and ubiquitous than ever before. With rapidly developing (neural) models and ever-more available data, current NLP models have access to more information than any human speaker … + In an election campaign, political parties pledge to implement various projects--should they be elected. But do they follow through? To track election pledges from parties' election manifestos, we need to distinguish between pledges and general …
    @@ -639,10 +639,10 @@

    Th
    -

    Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction

    +

    HONEST: Measuring Hurtful Sentence Completion in Language Models

    - We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines. + Language models have revolutionized the field of NLP. However, language models capture and proliferate hurtful stereotypes, especially in text generation. Our results show that **4.3% of the time, language models complete a sentence with a hurtful …
    @@ -651,10 +651,10 @@

    Language in a (Search
    -

    FEEL-IT: Emotion and Sentiment Classification for the Italian Language

    +

    The Importance of Modeling Social Factors of Language: Theory and Practice

    - Sentiment analysis is a common task to understand people's reactions online. Still, we often need more nuanced information: is the post negative because the user is angry or because they are sad? An abundance of approaches has been introduced for … + Natural language processing (NLP) applications are now more powerful and ubiquitous than ever before. With rapidly developing (neural) models and ever-more available data, current NLP models have access to more information than any human speaker …
    @@ -663,10 +663,10 @@

    FEEL-IT: Emot
    -

    MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?

    +

    Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction

    - The paper describes the MilaNLP team’s submission (Bocconi University, Milan) in the WASSA 2021 Shared Task on Empathy Detection and Emotion Classification. We focus on Track 2 - Emotion Classification - which consists of predicting the emotion of … + We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines.
    @@ -675,10 +675,10 @@

    MilaNLP @ WASSA: Does
    -

    Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning

    +

    FEEL-IT: Emotion and Sentiment Classification for the Italian Language

    - Supervised learning assumes that a ground truth label exists. However, the reliability of this ground truth depends on human annotators, who often disagree. Prior work has shown that this disagreement can be helpful in training models. We propose a … + Sentiment analysis is a common task to understand people's reactions online. Still, we often need more nuanced information: is the post negative because the user is angry or because they are sad? An abundance of approaches has been introduced for …
    @@ -687,10 +687,10 @@

    Beyond Black & White: Lev
    -

    Universal Joy A Data Set and Results for Classifying Emotions Across Languages

    +

    MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?

    - While emotions are universal aspects of human psychology, they are expressed differently across different languages and cultures. We introduce a new data set of over 530k anonymized public Facebook posts across 18 languages, labeled with five … + The paper describes the MilaNLP team’s submission (Bocconi University, Milan) in the WASSA 2021 Shared Task on Empathy Detection and Emotion Classification. We focus on Track 2 - Emotion Classification - which consists of predicting the emotion of …
    @@ -699,10 +699,10 @@

    Universal Joy A Data Set and Res
    -

    BERTective: Language Models and Contextual Information for Deception Detection

    +

    Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning

    - Spotting a lie is challenging but has an enormous potential impact on security as well as private and public safety. Several NLP methods have been proposed to classify texts as truthful or deceptive. In most cases, however, the target texts’ … + Supervised learning assumes that a ground truth label exists. However, the reliability of this ground truth depends on human annotators, who often disagree. Prior work has shown that this disagreement can be helpful in training models. We propose a …
    @@ -711,10 +711,10 @@

    BERTective: Language Models and Co
    -

    Cross-lingual Contextualized Topic Models with Zero-shot Learning

    +

    Universal Joy A Data Set and Results for Classifying Emotions Across Languages

    - We introduce a novel topic modeling method that can make use of contextulized embeddings (e.g., BERT) to do zero-shot cross-lingual topic modeling. + While emotions are universal aspects of human psychology, they are expressed differently across different languages and cultures. We introduce a new data set of over 530k anonymized public Facebook posts across 18 languages, labeled with five …
    @@ -723,10 +723,10 @@

    Cross-lingual Context
    -

    Text Analysis in Python for Social Scientists – Discovery and Exploration

    +

    BERTective: Language Models and Contextual Information for Deception Detection

    - Text is everywhere, and it is a fantastic resource for social scientists. However, because it is so abundant, and because language is so variable, it is often difficult to extract the information we want. There is a whole subfield of AI concerned … + Spotting a lie is challenging but has an enormous potential impact on security as well as private and public safety. Several NLP methods have been proposed to classify texts as truthful or deceptive. In most cases, however, the target texts’ …
    diff --git a/tags/nlp/page/8/index.html b/tags/nlp/page/8/index.html index a2156f63..9896fc56 100644 --- a/tags/nlp/page/8/index.html +++ b/tags/nlp/page/8/index.html @@ -271,7 +271,7 @@ - + @@ -615,10 +615,10 @@

    NLP

    -

    Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview

    +

    Cross-lingual Contextualized Topic Models with Zero-shot Learning

    - An increasing number of natural language processing papers address the effect of bias on predictions, introducing mitigation techniques at different parts of the standard NLP pipeline (data and models). However, these works have been conducted … + We introduce a novel topic modeling method that can make use of contextulized embeddings (e.g., BERT) to do zero-shot cross-lingual topic modeling.
    @@ -627,10 +627,10 @@

    Predictive Biases in Natural Language Pro
    -

    “You Sound Just Like Your Father” Commercial Machine Translation Systems Include Stylistic Biases

    +

    Text Analysis in Python for Social Scientists – Discovery and Exploration

    - The main goal of machine translation has been to convey the correct content. Stylistic considerations have been at best secondary. We show that as a consequence, the output of three commercial machine translation systems (Bing, DeepL, Google) make … + Text is everywhere, and it is a fantastic resource for social scientists. However, because it is so abundant, and because language is so variable, it is often difficult to extract the information we want. There is a whole subfield of AI concerned …
    @@ -639,10 +639,10 @@

    “You Sound Just Like Your Father” Comme
    -

    Visualizing Regional Language Variation Across Europe on Twitter

    +

    Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview

    - Geotagged Twitter data allows us to investigate correlations of geographic language variation, both at an interlingual and intralingual level. Based on data-driven studies of such relationships, this paper investigates regional variation of language … + An increasing number of natural language processing papers address the effect of bias on predictions, introducing mitigation techniques at different parts of the standard NLP pipeline (data and models). However, these works have been conducted …
    @@ -651,10 +651,10 @@

    Visualizing Regional Language Variat
    -

    Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success

    +

    “You Sound Just Like Your Father” Commercial Machine Translation Systems Include Stylistic Biases

    - When interacting with each other, we motivate, advise, inform, show love or power towards our peers. However, the way we interact may also hold some indication on how successful we are, as people often try to help each other to achieve their goals. … + The main goal of machine translation has been to convey the correct content. Stylistic considerations have been at best secondary. We show that as a consequence, the output of three commercial machine translation systems (Bing, DeepL, Google) make …
    @@ -663,10 +663,10 @@

    Helpful or Hierarchical? Predicting th
    -

    What the [MASK]? Making Sense of Language-Specific BERT Models

    +

    Visualizing Regional Language Variation Across Europe on Twitter

    - Recently, Natural Language Processing (NLP) has witnessed an impressive progress in many areas, due to the advent of novel, pretrained contextual representation models. In particular, Devlin et al. (2019) proposed a model, called BERT (Bidirectional … + Geotagged Twitter data allows us to investigate correlations of geographic language variation, both at an interlingual and intralingual level. Based on data-driven studies of such relationships, this paper investigates regional variation of language …
    @@ -675,10 +675,10 @@

    What the [MASK
    -

    INTEGRATOR

    +

    Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success

    - Incorporating Demographic Factors into Natural Language Processing Models + When interacting with each other, we motivate, advise, inform, show love or power towards our peers. However, the way we interact may also hold some indication on how successful we are, as people often try to help each other to achieve their goals. …
    @@ -687,10 +687,10 @@

    INTEGRATOR

    -

    MiMac

    +

    What the [MASK]? Making Sense of Language-Specific BERT Models

    - Mixed methods for analyzing political parties’ promises to voters during election campaigns + Recently, Natural Language Processing (NLP) has witnessed an impressive progress in many areas, due to the advent of novel, pretrained contextual representation models. In particular, Devlin et al. (2019) proposed a model, called BERT (Bidirectional …
    @@ -699,10 +699,10 @@

    MiMac

    -

    MONICA

    +

    INTEGRATOR

    - MONItoring Coverage, Attitudes and Accessibility of Italian measures in response to COVID-19 + Incorporating Demographic Factors into Natural Language Processing Models
    @@ -711,10 +711,10 @@

    MONICA
    -

    Twitter Healthy Conversations

    +

    MiMac

    - Devising Metrics for Assessing Echo Chambers, Incivility, and Intolerance on Twitter + Mixed methods for analyzing political parties’ promises to voters during election campaigns
    @@ -723,10 +723,10 @@

    Twitter Healthy Conversations

    -

    A Case for Soft Loss Functions

    +

    MONICA

    - Recently, Peterson et al. provided evidence of the benefits of using probabilistic soft labels generated from crowd annotations for training a computer vision model, showing that using such labels maximizes performance of the models over unseen data. … + MONItoring Coverage, Attitudes and Accessibility of Italian measures in response to COVID-19
    diff --git a/tags/nlp/page/9/index.html b/tags/nlp/page/9/index.html index 8fca37f7..35b0455e 100644 --- a/tags/nlp/page/9/index.html +++ b/tags/nlp/page/9/index.html @@ -271,7 +271,7 @@ - + @@ -615,10 +615,10 @@

    NLP

    -

    Fake opinion detection: how similar are crowdsourced datasets to real data?

    +

    Twitter Healthy Conversations

    - Identifying deceptive online reviews is a challenging tasks for Natural Language Processing (NLP). Collecting corpora for the task is difficult, because normally it is not possible to know whether reviews are genuine. A common workaround involves … + Devising Metrics for Assessing Echo Chambers, Incivility, and Intolerance on Twitter
    @@ -627,10 +627,10 @@

    Fake opinion detection: how similar are cr
    -

    Dense Node Representation for Geolocation

    +

    A Case for Soft Loss Functions

    - Prior research has shown that geolocation can be substantially improved by including user network information. While effective, it suffers from the curse of dimensionality, since networks are usually represented as sparse adjacency matrices of … + Recently, Peterson et al. provided evidence of the benefits of using probabilistic soft labels generated from crowd annotations for training a computer vision model, showing that using such labels maximizes performance of the models over unseen data. …
    @@ -639,10 +639,10 @@

    Dense Node Representation for Geolocation<
    -

    Geolocation with Attention-Based Multitask Learning Models

    +

    Fake opinion detection: how similar are crowdsourced datasets to real data?

    - Geolocation, predicting the location of a post based on text and other information, has a huge potential for several social media applications. Typically, the problem is modeled as either multi-class classification or regression. In the first case, … + Identifying deceptive online reviews is a challenging tasks for Natural Language Processing (NLP). Collecting corpora for the task is difficult, because normally it is not possible to know whether reviews are genuine. A common workaround involves …
    @@ -651,10 +651,10 @@

    Geolocation with Attention-Based Multi
    -

    Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers

    +

    Dense Node Representation for Geolocation

    - User reviews provide a significant source of information for companies to understand their market and audience. In order to discover broad trends in this source, researchers have typically used topic models such as Latent Dirichlet Allocation (LDA). … + Prior research has shown that geolocation can be substantially improved by including user network information. While effective, it suffers from the curse of dimensionality, since networks are usually represented as sparse adjacency matrices of …
    @@ -663,10 +663,10 @@

    Hey Siri. Ok Google. Alexa: A topic model
    -

    Identifying Linguistic Areas for Geolocation

    +

    Geolocation with Attention-Based Multitask Learning Models

    - Geolocating social media posts relies on the assumption that language carries sufficient geographic information. However, locations are usually given as continuous latitude/longitude tuples, so we first need to define discrete geographic regions … + Geolocation, predicting the location of a post based on text and other information, has a huge potential for several social media applications. Typically, the problem is modeled as either multi-class classification or regression. In the first case, …
    @@ -675,10 +675,10 @@

    Identifying Linguistic Areas for Geolocati
    -

    Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing

    +

    Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers

    - Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have still not adapted to account for … + User reviews provide a significant source of information for companies to understand their market and audience. In order to discover broad trends in this source, researchers have typically used topic models such as Latent Dirichlet Allocation (LDA). …
    @@ -687,10 +687,10 @@

    Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting

    +

    Identifying Linguistic Areas for Geolocation

    - Dialects are one of the main drivers of language variation, a major challenge for natural language processing tools. In most languages, dialects exist along a continuum, and are commonly discretized by combining the extent of several preselected … + Geolocating social media posts relies on the assumption that language carries sufficient geographic information. However, locations are usually given as continuous latitude/longitude tuples, so we first need to define discrete geographic regions …
    @@ -699,10 +699,10 @@

    Comparing Bayesian Models of Annotation

    +

    Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing

    - The analysis of crowdsourced annotations in natural language processing is concerned with identifying (1) gold standard labels, (2) annotator accuracies and biases, and (3) item difficulties and error patterns. Traditionally, majority voting was used … + Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have still not adapted to account for …
    @@ -711,10 +711,10 @@

    Comparing
    -

    Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning

    +

    Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting

    - Newspapers need to attract readers with headlines, anticipating their readers’ preferences. These preferences rely on topical, structural, and lexical factors. We model each of these factors in a multi-task GRU network to predict headline popularity. … + Dialects are one of the main drivers of language variation, a major challenge for natural language processing tools. In most languages, dialects exist along a continuum, and are commonly discretized by combining the extent of several preselected …
    @@ -723,10 +723,10 @@

    The Social and the Neural Network: How to Make Natural Language Processing about People again

    +

    Comparing Bayesian Models of Annotation

    - Over the years, natural language processing has increasingly focused on tasks that can be solved by statistical models, but ignored the social aspects of language. These limitations are in large part due to historically available data and the … + The analysis of crowdsourced annotations in natural language processing is concerned with identifying (1) gold standard labels, (2) annotator accuracies and biases, and (3) item difficulties and error patterns. Traditionally, majority voting was used …
    @@ -739,6 +739,8 @@

    The Social and the Neura
  • «
    • +
  • »
    • + diff --git a/tags/online-hate/index.html b/tags/online-hate/index.html new file mode 100644 index 00000000..e695aead --- /dev/null +++ b/tags/online-hate/index.html @@ -0,0 +1,792 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Online Hate | MilaNLP Lab @ Bocconi University + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +

    Online Hate

    + + + + +
    + + + +
    + + + + + + + +
    +

    From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets

    +
    + + Perceptions of hate can vary greatly across cultural contexts. Hate speech (HS) datasets, however, have traditionally been developed by language. This hides potential cultural biases, as one language may be spoken in different countries home to … + +
    +
    + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + + + + + + + diff --git a/tags/online-hate/index.xml b/tags/online-hate/index.xml new file mode 100644 index 00000000..5d22ce20 --- /dev/null +++ b/tags/online-hate/index.xml @@ -0,0 +1,24 @@ + + + + Online Hate | MilaNLP Lab @ Bocconi University + https://milanlproc.github.io/tags/online-hate/ + + Online Hate + Source Themes Academic (https://sourcethemes.com/academic/)en-us© MilaNLP, 2024Sat, 20 Jul 2024 00:00:00 +0000 + + img/map[gravatar:%!s(bool=false) shape:circle] + Online Hate + https://milanlproc.github.io/tags/online-hate/ + + + + From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets + https://milanlproc.github.io/publication/2024-hate-geographies/ + Sat, 20 Jul 2024 00:00:00 +0000 + https://milanlproc.github.io/publication/2024-hate-geographies/ + + + + + diff --git a/tags/online-hate/page/1/index.html b/tags/online-hate/page/1/index.html new file mode 100644 index 00000000..9945ca58 --- /dev/null +++ b/tags/online-hate/page/1/index.html @@ -0,0 +1 @@ +https://milanlproc.github.io/tags/online-hate/ \ No newline at end of file diff --git a/tags/page/10/index.html b/tags/page/10/index.html index 61b1f6dc..2afd70a2 100644 --- a/tags/page/10/index.html +++ b/tags/page/10/index.html @@ -271,7 +271,7 @@ - + @@ -615,7 +615,7 @@

    Tags

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    disagreement

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    text analysis

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    Coherence

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    Issues

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    Position Paper

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    Coherence

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    Issues

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    topic modeling

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    Election pledges

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    Position Paper

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    Election pledges

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    Zero-Shot Learning

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    topic modeling

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    social factors

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    Zero-Shot Learning

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    computational linguistics

    diff --git a/tags/page/11/index.html b/tags/page/11/index.html index fab5d9b6..3d9e16b9 100644 --- a/tags/page/11/index.html +++ b/tags/page/11/index.html @@ -271,7 +271,7 @@ - + @@ -615,7 +615,7 @@

    Tags

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    Linguistics

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    social factors

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    Linguistics

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    embeddings

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    Linguistics

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    data set

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    deception detection

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    Soft-labels

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    deception detection

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    computational sociolinguistics

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    data set

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    computational sociolinguist
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    representation learning

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    deception detection

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    representation learning

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    sociolinguistics

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    computational sociolinguistics

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    sociolinguistics

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    communication

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    representation learning

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    sociolinguistics

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    style

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    communication

    diff --git a/tags/page/12/index.html b/tags/page/12/index.html index 3eafad99..438fe3ca 100644 --- a/tags/page/12/index.html +++ b/tags/page/12/index.html @@ -271,7 +271,7 @@ - + @@ -615,7 +615,7 @@

    Tags

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    computational social sciences

    +

    conversation

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    computational social science
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    loss function

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    style

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    loss function

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    clustering

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    computational social sciences

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    clustering

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    geolocation

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    loss function

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    geolocation

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    multitask learning

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    clustering

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    multitask learning

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    smart speakers

    +

    geolocation

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    smart speakers

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    parsing

    +

    multitask learning

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    parsing

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    pos tagging

    +

    smart speakers

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    pos tagging

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    generative models

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    parsing

    @@ -705,7 +705,7 @@

    generative models

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    retrofitting

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    pos tagging

    diff --git a/tags/page/13/index.html b/tags/page/13/index.html index c1c435a5..83626e11 100644 --- a/tags/page/13/index.html +++ b/tags/page/13/index.html @@ -271,7 +271,7 @@ - + @@ -614,6 +614,26 @@

    Tags

    +
    +

    generative models

    +
    + +
    +
    + + + + +
    +

    retrofitting

    +
    + +
    +
    + + + +

    text classification

    diff --git a/tags/page/2/index.html b/tags/page/2/index.html index 0e9597f9..189a9163 100644 --- a/tags/page/2/index.html +++ b/tags/page/2/index.html @@ -271,7 +271,7 @@ - + @@ -615,7 +615,7 @@

    Tags

    -

    Language Models

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    Online Hate

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    Language Models

    -

    multimodal classification

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    demographic prediction

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    multimodal classification -

    Twitter data

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    Language Models

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    Twitter data

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    Difficulty Classification

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    multimodal classification

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    Difficulty Classification -

    Education

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    Twitter data

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    Education

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    Adaptive Learning

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    Difficulty Classification

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    Adaptive Learning

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    Conversational Data

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    Education

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    Conversational Data

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    Emotion analysis

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    Adaptive Learning

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    Emotion analysis

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    Gender Bias

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    Conversational Data

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    Gender Bias

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    Natural Language Processing

    +

    Emotion analysis

    diff --git a/tags/page/3/index.html b/tags/page/3/index.html index 9117ab89..a51a35c6 100644 --- a/tags/page/3/index.html +++ b/tags/page/3/index.html @@ -271,7 +271,7 @@ - + @@ -615,7 +615,7 @@

    Tags

    -

    Survey

    +

    Gender Bias

    @@ -625,7 +625,7 @@

    Survey

    -

    demographics

    +

    Natural Language Processing

    @@ -635,7 +635,7 @@

    demographics

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    Fairness

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    Survey

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    Fairness

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    hate speech

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    demographics

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    hate speech

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    Offensiveness

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    Fairness

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    hate speech

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    Sexism

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    Offensiveness

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    Social class

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    subjectivity

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    Sexism

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    subjectivity

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    interpretability

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    Social class

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    interpretability

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    machine translation

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    subjectivity

    diff --git a/tags/page/4/index.html b/tags/page/4/index.html index ff0405c7..182eda37 100644 --- a/tags/page/4/index.html +++ b/tags/page/4/index.html @@ -271,7 +271,7 @@ - + @@ -615,7 +615,7 @@

    Tags

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    Anthropomorphism

    +

    interpretability

    @@ -625,7 +625,7 @@

    Anthropomorphism

    -

    Dialogue systems

    +

    machine translation

    @@ -635,7 +635,7 @@

    Dialogue systems

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    Trust

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    Anthropomorphism

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    Alignment

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    Dialogue systems

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    Human Feedback

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    Trust

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    annotation

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    Alignment

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    annotation

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    LLMs

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    Human Feedback

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    domain adaptation

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    annotation

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    ethics

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    LLMs

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    domain adaptation

    diff --git a/tags/page/5/index.html b/tags/page/5/index.html index e407343a..a5d4e63f 100644 --- a/tags/page/5/index.html +++ b/tags/page/5/index.html @@ -271,7 +271,7 @@ - + @@ -615,7 +615,7 @@

    Tags

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    Multimodal

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    ethics

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    pronouns

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    multilingual

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    sociodemographics

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    Multimodal

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    Empathy

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    pronouns

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    sociodemographics

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    networks

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    Empathy

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    misinformation

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    networks

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    social media

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    computational social science<
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    culture

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    Vision

    diff --git a/tags/page/6/index.html b/tags/page/6/index.html index 35a36089..988982e5 100644 --- a/tags/page/6/index.html +++ b/tags/page/6/index.html @@ -271,7 +271,7 @@ - + @@ -615,7 +615,7 @@

    Tags

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    BERT

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    computational social science

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    BERT

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    demographic

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    culture

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    echo chambers

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    BERT

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    political science

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    demographic

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    AI

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    echo chambers

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    AI

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    models

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    political science

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    AI

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    models

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    Twitter

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    sociology

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    sustainability

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    dataset

    diff --git a/tags/page/7/index.html b/tags/page/7/index.html index 7f689fda..4b973e6b 100644 --- a/tags/page/7/index.html +++ b/tags/page/7/index.html @@ -271,7 +271,7 @@ - + @@ -615,7 +615,7 @@

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    transformers

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    Twitter

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    chatbots

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    sustainability

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    NLG

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    transformers

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    Architectures

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    PerceiverIO

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    Meme

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    Language Invariant Properties

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    Misogyny

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    economics

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    inequality

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    Sentiment Analysis

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    Emotion Detection

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    Sentiment Analysis

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    Sentiment Analysis

    diff --git a/tags/page/9/index.html b/tags/page/9/index.html index 21c65e5b..353b69b5 100644 --- a/tags/page/9/index.html +++ b/tags/page/9/index.html @@ -271,7 +271,7 @@ - + @@ -615,7 +615,7 @@

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    Domain Specialization

    +

    Computational Argumentation

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    Domain Specialization

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    Attention

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    Conversational AI

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    Domain Specialization

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    classification

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    Entropy

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    Regularization

    @@ -685,7 +685,7 @@

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