Update metadata from Papers with Code

EricPeter · Aug 4, 2022 · f757316 · f757316
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diff --git a/data/xml/2020.aacl.xml b/data/xml/2020.aacl.xml
@@ -120,6 +120,7 @@
       <bibkey>wu-etal-2020-towards</bibkey>
       <pwcdataset url="https://paperswithcode.com/dataset/glue">GLUE</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/mrpc">MRPC</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sst">SST</pwcdataset>
     </paper>
     <paper id="10">
@@ -145,6 +146,7 @@
       <abstract>The recently introduced pre-trained language model BERT advances the state-of-the-art on many NLP tasks through the fine-tuning approach, but few studies investigate how the fine-tuning process improves the model performance on downstream tasks. In this paper, we inspect the learning dynamics of BERT fine-tuning with two indicators. We use JS divergence to detect the change of the attention mode and use SVCCA distance to examine the change to the feature extraction mode during BERT fine-tuning. We conclude that BERT fine-tuning mainly changes the attention mode of the last layers and modifies the feature extraction mode of the intermediate and last layers. Moreover, we analyze the consistency of BERT fine-tuning between different random seeds and different datasets. In summary, we provide a distinctive understanding of the learning dynamics of BERT fine-tuning, which sheds some light on improving the fine-tuning results.</abstract>
       <url hash="d60ccad2">2020.aacl-main.11</url>
       <bibkey>hao-etal-2020-investigating</bibkey>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sst">SST</pwcdataset>
     </paper>
     <paper id="12">
@@ -709,6 +711,7 @@
       <pwcdataset url="https://paperswithcode.com/dataset/cosmosqa">CosmosQA</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/hellaswag">HellaSwag</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/mlqa">MLQA</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/paws-x">PAWS-X</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/snli">SNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/squad">SQuAD</pwcdataset>
@@ -853,6 +856,7 @@
       <pwccode url="https://github.com/nyu-mll/semi-automatic-nli" additional="false">nyu-mll/semi-automatic-nli</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/anli">ANLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/copa">COPA</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/multirc">MultiRC</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/snli">SNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/superglue">SuperGLUE</pwcdataset>

diff --git a/data/xml/2020.acl.xml b/data/xml/2020.acl.xml
@@ -3125,6 +3125,7 @@
       <video href="http://slideslive.com/38928832"/>
       <bibkey>min-etal-2020-syntactic</bibkey>
       <pwccode url="https://github.com/aatlantise/syntactic-augmentation-nli" additional="false">aatlantise/syntactic-augmentation-nli</pwccode>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
     </paper>
     <paper id="213">
       <title>Improved Speech Representations with Multi-Target Autoregressive Predictive Coding</title>
@@ -3597,6 +3598,7 @@
       <bibkey>hendrycks-etal-2020-pretrained</bibkey>
       <pwccode url="https://github.com/camelop/NLP-Robustness" additional="false">camelop/NLP-Robustness</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/imdb-movie-reviews">IMDb Movie Reviews</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/record">ReCoRD</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/snli">SNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sst">SST</pwcdataset>
@@ -3616,6 +3618,7 @@
       <pwccode url="https://worksheets.codalab.org/worksheets/0x8fc01c7fc2b742fdb29c05669f0ad7d2" additional="false">worksheets/0x8fc01c7f</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/glue">GLUE</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/mrpc">MRPC</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/qnli">QNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sst">SST</pwcdataset>
     </paper>
@@ -3691,6 +3694,7 @@
       <doi>10.18653/v1/2020.acl-main.250</doi>
       <video href="http://slideslive.com/38929349"/>
       <bibkey>khetan-karnin-2020-schubert</bibkey>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
     </paper>
     <paper id="251">
       <title><fixed-case>ENGINE</fixed-case>: Energy-Based Inference Networks for Non-Autoregressive Machine Translation</title>
@@ -3854,6 +3858,7 @@
       <doi>10.18653/v1/2020.acl-main.262</doi>
       <video href="http://slideslive.com/38928838"/>
       <bibkey>ethayarajh-2020-classifier</bibkey>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/winobias">WinoBias</pwcdataset>
     </paper>
     <paper id="263">
@@ -4555,6 +4560,7 @@
       <pwccode url="https://github.com/uclanlp/ProbeGrammarRobustness" additional="false">uclanlp/ProbeGrammarRobustness</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/glue">GLUE</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/mrpc">MRPC</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/qnli">QNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sst">SST</pwcdataset>
     </paper>
@@ -5392,6 +5398,7 @@
       <bibkey>schick-schutze-2020-bertram</bibkey>
       <pwccode url="https://github.com/timoschick/bertram" additional="false">timoschick/bertram</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/bookcorpus">BookCorpus</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/wnlampro">WNLaMPro</pwcdataset>
     </paper>
     <paper id="369">
@@ -5435,6 +5442,7 @@
       <video href="http://slideslive.com/38929133"/>
       <bibkey>bar-haim-etal-2020-arguments</bibkey>
       <pwcdataset url="https://paperswithcode.com/dataset/ibm-rank-30k">IBM-Rank-30k</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/snli">SNLI</pwcdataset>
     </paper>
     <paper id="372">
@@ -6020,6 +6028,7 @@
       <bibkey>cao-etal-2020-deformer</bibkey>
       <pwccode url="https://github.com/StonyBrookNLP/deformer" additional="false">StonyBrookNLP/deformer</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/boolq">BoolQ</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/race">RACE</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/squad">SQuAD</pwcdataset>
     </paper>
@@ -6209,6 +6218,7 @@
       <bibkey>wu-etal-2020-similarity</bibkey>
       <pwccode url="https://github.com/johnmwu/contextual-corr-analysis" additional="false">johnmwu/contextual-corr-analysis</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/glue">GLUE</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sst">SST</pwcdataset>
     </paper>
     <paper id="423">
@@ -6508,6 +6518,7 @@
       <pwcdataset url="https://paperswithcode.com/dataset/glue">GLUE</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/hotpotqa">HotpotQA</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/imagenet">ImageNet</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/snli">SNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/squad">SQuAD</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/swag">SWAG</pwcdataset>
@@ -6603,7 +6614,7 @@
       <doi>10.18653/v1/2020.acl-main.447</doi>
       <video href="http://slideslive.com/38929131"/>
       <bibkey>lo-etal-2020-s2orc</bibkey>
-      <pwccode url="https://github.com/allenai/s2-gorc" additional="true">allenai/s2-gorc</pwccode>
+      <pwccode url="https://github.com/allenai/s2orc" additional="true">allenai/s2orc</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/s2orc">S2ORC</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/cord-19">CORD-19</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/dblp">DBLP</pwcdataset>
@@ -7257,6 +7268,7 @@
       <video href="http://slideslive.com/38929233"/>
       <bibkey>han-etal-2020-explaining</bibkey>
       <pwccode url="https://github.com/xhan77/influence-function-analysis" additional="false">xhan77/influence-function-analysis</pwccode>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sst">SST</pwcdataset>
     </paper>
     <paper id="493">
@@ -8773,6 +8785,7 @@
       <bibkey>schwartz-etal-2020-right</bibkey>
       <pwccode url="https://github.com/allenai/sledgehammer" additional="false">allenai/sledgehammer</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/imdb-movie-reviews">IMDb Movie Reviews</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/snli">SNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sst">SST</pwcdataset>
     </paper>
@@ -10378,6 +10391,7 @@
       <pwcdataset url="https://paperswithcode.com/dataset/convai2">ConvAI2</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/eli5">ELI5</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/glue">GLUE</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/squad">SQuAD</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/xsum">XSum</pwcdataset>
     </paper>
@@ -11352,6 +11366,7 @@
       <bibkey>karimi-mahabadi-etal-2020-end</bibkey>
       <pwccode url="https://github.com/rabeehk/robust-nli" additional="true">rabeehk/robust-nli</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/glue">GLUE</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sick">SICK</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/snli">SNLI</pwcdataset>
     </paper>
@@ -11367,6 +11382,7 @@
       <video href="http://slideslive.com/38929067"/>
       <bibkey>utama-etal-2020-mind</bibkey>
       <pwccode url="https://github.com/UKPLab/acl2020-confidence-regularization" additional="false">UKPLab/acl2020-confidence-regularization</pwccode>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/paws">PAWS</pwcdataset>
     </paper>
     <paper id="771">
@@ -11380,6 +11396,7 @@
       <video href="http://slideslive.com/38929362"/>
       <bibkey>kumar-talukdar-2020-nile</bibkey>
       <pwccode url="https://github.com/SawanKumar28/nile" additional="false">SawanKumar28/nile</pwccode>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/snli">SNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/e-snli">e-SNLI</pwcdataset>
     </paper>
@@ -11396,6 +11413,7 @@
       <bibkey>he-etal-2020-quase</bibkey>
       <pwccode url="https://github.com/CogComp/QuASE" additional="false">CogComp/QuASE</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/glue">GLUE</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/newsqa">NewsQA</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/qa-srl">QA-SRL</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/qamr">QAMR</pwcdataset>
@@ -11413,6 +11431,7 @@
       <video href="http://slideslive.com/38929270"/>
       <bibkey>zhou-bansal-2020-towards</bibkey>
       <pwccode url="https://github.com/owenzx/LexicalDebias-ACL2020" additional="false">owenzx/LexicalDebias-ACL2020</pwccode>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
     </paper>
     <paper id="774">
       <title>Uncertain Natural Language Inference</title>
@@ -11752,6 +11771,7 @@
       <pwcdataset url="https://paperswithcode.com/dataset/commonsenseqa">CommonsenseQA</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/glue">GLUE</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/hellaswag">HellaSwag</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/squad">SQuAD</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/swag">SWAG</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/social-iqa">Social IQA</pwcdataset>
@@ -11780,6 +11800,7 @@
       <pwcdataset url="https://paperswithcode.com/dataset/anli">ANLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/glue">GLUE</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/mrpc">MRPC</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/qnli">QNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/snli">SNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/squad">SQuAD</pwcdataset>
@@ -12405,6 +12426,7 @@
       <bibkey>harbecke-alt-2020-considering</bibkey>
       <pwccode url="https://github.com/DFKI-NLP/OLM" additional="false">DFKI-NLP/OLM</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/cola">CoLA</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sst">SST</pwcdataset>
     </paper>
     <paper id="17">

diff --git a/data/xml/2020.blackboxnlp.xml b/data/xml/2020.blackboxnlp.xml
@@ -67,6 +67,7 @@
       <video href="https://slideslive.com/38939763"/>
       <bibkey>merchant-etal-2020-happens</bibkey>
       <pwcdataset url="https://paperswithcode.com/dataset/glue">GLUE</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/squad">SQuAD</pwcdataset>
     </paper>
     <paper id="5">
@@ -167,6 +168,7 @@
       <attachment type="OptionalSupplementaryMaterial" hash="437d7ae3">2020.blackboxnlp-1.12.OptionalSupplementaryMaterial.pdf</attachment>
       <doi>10.18653/v1/2020.blackboxnlp-1.12</doi>
       <bibkey>li-etal-2020-linguistically</bibkey>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
     </paper>
     <paper id="13">
       <title>Controlling the Imprint of Passivization and Negation in Contextualized Representations</title>
@@ -217,6 +219,7 @@
       <bibkey>geiger-etal-2020-neural</bibkey>
       <pwccode url="https://github.com/atticusg/MoNLI" additional="false">atticusg/MoNLI</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/help">HELP</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/snli">SNLI</pwcdataset>
     </paper>
     <paper id="17">
@@ -281,6 +284,7 @@
       <doi>10.18653/v1/2020.blackboxnlp-1.21</doi>
       <video href="https://slideslive.com/38939766"/>
       <bibkey>mccoy-etal-2020-berts</bibkey>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
     </paper>
     <paper id="22">
       <title>Second-Order <fixed-case>NLP</fixed-case> Adversarial Examples</title>

diff --git a/data/xml/2020.calcs.xml b/data/xml/2020.calcs.xml
@@ -40,6 +40,7 @@
       <language>eng</language>
       <bibkey>khanuja-etal-2020-new</bibkey>
       <pwcdataset url="https://paperswithcode.com/dataset/glue">GLUE</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/multinli">MultiNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/snli">SNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/xnli">XNLI</pwcdataset>
     </paper>