From 62614a1841c840052aa00820a60ed876a587d2da Mon Sep 17 00:00:00 2001
From: acl-pwc-bot <94475230+acl-pwc-bot@users.noreply.github.com>
Date: Thu, 1 Sep 2022 03:07:14 +0200
Subject: [PATCH] Update metadata from Papers with Code
---
data/xml/2020.blackboxnlp.xml | 1 +
data/xml/2020.findings.xml | 2 +-
data/xml/2020.lrec.xml | 1 +
data/xml/2020.trac.xml | 1 +
data/xml/2021.crac.xml | 2 +-
data/xml/2021.econlp.xml | 2 +-
data/xml/2021.gwc.xml | 2 ++
data/xml/2022.acl.xml | 1 +
data/xml/2022.findings.xml | 3 ++-
data/xml/2022.naacl.xml | 3 ++-
data/xml/W16.xml | 1 +
data/xml/W18.xml | 1 +
12 files changed, 15 insertions(+), 5 deletions(-)
diff --git a/data/xml/2020.blackboxnlp.xml b/data/xml/2020.blackboxnlp.xml
index 981e20995e..40e6d02d12 100644
--- a/data/xml/2020.blackboxnlp.xml
+++ b/data/xml/2020.blackboxnlp.xml
@@ -141,6 +141,7 @@
2020.blackboxnlp-1.10.OptionalSupplementaryMaterial.zip
10.18653/v1/2020.blackboxnlp-1.10
treviso-martins-2020-explanation
+ deep-spin/spec
e-SNLI
diff --git a/data/xml/2020.findings.xml b/data/xml/2020.findings.xml
index a504ee4009..3ef65152ac 100644
--- a/data/xml/2020.findings.xml
+++ b/data/xml/2020.findings.xml
@@ -4458,7 +4458,7 @@
2020.findings-emnlp.301
10.18653/v1/2020.findings-emnlp.301
gehman-etal-2020-realtoxicityprompts
-
+ allenai/real-toxicity-prompts
WebText
diff --git a/data/xml/2020.lrec.xml b/data/xml/2020.lrec.xml
index f4dda3713c..9e86a31785 100644
--- a/data/xml/2020.lrec.xml
+++ b/data/xml/2020.lrec.xml
@@ -7266,6 +7266,7 @@
2020.lrec-1.578
eng
amin-nejad-etal-2020-exploring
+ amin-nejad/mimic-website
Multi-lingual Mathematical Word Problem Generation using Long Short Term Memory Networks with Enhanced Input Features
diff --git a/data/xml/2020.trac.xml b/data/xml/2020.trac.xml
index 78299bd084..64a95cbfe2 100644
--- a/data/xml/2020.trac.xml
+++ b/data/xml/2020.trac.xml
@@ -163,6 +163,7 @@
2020.trac-1.12
eng
baruah-etal-2020-aggression
+ Urdu Online Reviews
<fixed-case>L</fixed-case>a<fixed-case>STUS</fixed-case>/<fixed-case>TALN</fixed-case> at <fixed-case>TRAC</fixed-case> - 2020 Trolling, Aggression and Cyberbullying
diff --git a/data/xml/2021.crac.xml b/data/xml/2021.crac.xml
index b15549a8ca..a807688a98 100644
--- a/data/xml/2021.crac.xml
+++ b/data/xml/2021.crac.xml
@@ -168,7 +168,7 @@
2021.crac-1.12
toshniwal-etal-2021-generalization
10.18653/v1/2021.crac-1.12
- shtoshni92/fast-coref
+ shtoshni92/fast-coref
GAP Coreference Dataset
OntoGUM
PreCo
diff --git a/data/xml/2021.econlp.xml b/data/xml/2021.econlp.xml
index f2a4b3f68f..4a53330a6d 100644
--- a/data/xml/2021.econlp.xml
+++ b/data/xml/2021.econlp.xml
@@ -37,7 +37,7 @@
2021.econlp-1.2
loukas-etal-2021-edgar
10.18653/v1/2021.econlp-1.2
- nlpaueb/edgar-crawler
+ nlpaueb/edgar-crawler
EDGAR-CORPUS
diff --git a/data/xml/2021.gwc.xml b/data/xml/2021.gwc.xml
index 29c53313e7..165c2b63ed 100644
--- a/data/xml/2021.gwc.xml
+++ b/data/xml/2021.gwc.xml
@@ -36,6 +36,8 @@
The results reported in this paper aim to increase the presence of the Uzbek language in the Internet and its usability within IT applications. We describe the initial development of a “word-net” for the Uzbek language compatible to Princeton WordNet. We called it UZWORDNET. In the current version, UZWORDNET contains 28140 synsets, 64389 sense and 20683 words; its estimated accuracy is 75.98%. To the best of our knowledge, it is the largest wordnet for Uzbek existing to date, and the second wordnet developed overall.
2021.gwc-1.2
agostini-etal-2021-uzwordnet
+ LDKR-Group/UzWordnet
+ UzWordnet
Practical Approach on Implementation of <fixed-case>W</fixed-case>ord<fixed-case>N</fixed-case>ets for <fixed-case>S</fixed-case>outh <fixed-case>A</fixed-case>frican Languages
diff --git a/data/xml/2022.acl.xml b/data/xml/2022.acl.xml
index 74ef15c7ee..7ad5421ee3 100644
--- a/data/xml/2022.acl.xml
+++ b/data/xml/2022.acl.xml
@@ -5877,6 +5877,7 @@ in the Case of Unambiguous Gender
2022.acl-long.379
falk-lapesa-2022-reports
10.18653/v1/2022.acl-long.379
+ blubberli/storytestimony
Non-neural Models Matter: a Re-evaluation of Neural Referring Expression Generation Systems
diff --git a/data/xml/2022.findings.xml b/data/xml/2022.findings.xml
index d48675abdb..d544a15222 100644
--- a/data/xml/2022.findings.xml
+++ b/data/xml/2022.findings.xml
@@ -5640,7 +5640,7 @@
2022.findings-naacl.47
nadejde-etal-2022-cocoa
10.18653/v1/2022.findings-naacl.47
- awslabs/sockeye
+ awslabs/sockeye
<fixed-case>CLEAR</fixed-case>: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations
@@ -6348,6 +6348,7 @@
2022.findings-naacl.92
gao-etal-2022-retrieval
10.18653/v1/2022.findings-naacl.92
+ yifan-gao/multilingual_keyphrase_generation
KP20k
Microsoft Academic Graph
diff --git a/data/xml/2022.naacl.xml b/data/xml/2022.naacl.xml
index 79fe0501e4..edf9267266 100644
--- a/data/xml/2022.naacl.xml
+++ b/data/xml/2022.naacl.xml
@@ -855,6 +855,7 @@
2022.naacl-main.57
lu-etal-2022-neurologic
10.18653/v1/2022.naacl-main.57
+ GXimingLu/a_star_neurologic
CommonGen
ROCStories
@@ -7822,7 +7823,7 @@
2022.naacl-industry.24
ayoola-etal-2022-refined
10.18653/v1/2022.naacl-industry.24
- alexa/refined
+ amazon-research/ReFinED
ACE 2004
AIDA CoNLL-YAGO
AQUAINT
diff --git a/data/xml/W16.xml b/data/xml/W16.xml
index 98ae37a06d..7dd552142e 100644
--- a/data/xml/W16.xml
+++ b/data/xml/W16.xml
@@ -6556,6 +6556,7 @@
W16-3210
10.18653/v1/W16-3210
elliott-etal-2016-multi30k
+
Flickr30k
diff --git a/data/xml/W18.xml b/data/xml/W18.xml
index 59331666e4..4a95f74eb6 100644
--- a/data/xml/W18.xml
+++ b/data/xml/W18.xml
@@ -7316,6 +7316,7 @@
This paper describes our participation in the First Shared Task on Aggression Identification. The method proposed relies on machine learning to identify social media texts which contain aggression. The main features employed by our method are information extracted from word embeddings and the output of a sentiment analyser. Several machine learning methods and different combinations of features were tried. The official submissions used Support Vector Machines and Random Forests. The official evaluation showed that for texts similar to the ones in the training dataset Random Forests work best, whilst for texts which are different SVMs are a better choice. The evaluation also showed that despite its simplicity the method performs well when compared with more elaborated methods.
orasan-2018-aggressive
dinel/aggression_identification
+ 200 People-Chinese Speech Data by Mobile Phone
Aggression Detection in Social Media using Deep Neural Networks