Merge branch 'master' into merge-from-acl-anthology-py

data/xml/2018.icon.xml

@@ -190,7 +190,7 @@
     <paper id="20">
       <title>Improving Computer Generated Dialog with Auxiliary Loss Functions and Custom Evaluation Metrics</title>
       <author><first>Thomas</first><last>Conley</last></author>
-      <author><first>Jack St.</first><last>Clair</last></author>
+      <author><first>Jack</first><last>St. Clair</last></author>
       <author><first>Jugal</first><last>Kalita</last></author>
       <pages>138–144</pages>
       <url hash="db18d5dc">2018.icon-1.20</url>

data/xml/2020.aacl.xml

@@ -621,6 +621,7 @@
       <url hash="545b660a">2020.aacl-main.49</url>
       <bibkey>gao-etal-2020-systematic</bibkey>
       <pwccode url="https://github.com/HQ01/gSCAN_with_language_conditioned_embedding" additional="true">HQ01/gSCAN_with_language_conditioned_embedding</pwccode>
+      <pwcdataset url="https://paperswithcode.com/dataset/gscan">GSCAN</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/scan">SCAN</pwcdataset>
     </paper>
     <paper id="50">
@@ -1548,7 +1549,7 @@
       <abstract>We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T) modeling tasks such as end-to-end speech recognition and speech-to-text translation. It follows fairseq’s careful design for scalability and extensibility. We provide end-to-end workflows from data pre-processing, model training to offline (online) inference. We implement state-of-the-art RNN-based as well as Transformer-based models and open-source detailed training recipes. Fairseq’s machine translation models and language models can be seamlessly integrated into S2T workflows for multi-task learning or transfer learning. Fairseq S2T is available at <url>https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text</url>.</abstract>
       <url hash="ba6e2aa3">2020.aacl-demo.6</url>
       <bibkey>wang-etal-2020-fairseq</bibkey>
-      <pwccode url="https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text" additional="true">pytorch/fairseq</pwccode>
+      <pwccode url="https://github.com/pytorch/fairseq" additional="true">pytorch/fairseq</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/librispeech">LibriSpeech</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/must-c">MuST-C</pwcdataset>
     </paper>

data/xml/2020.acl.xml

@@ -673,7 +673,7 @@
       <video href="http://slideslive.com/38928694"/>
       <bibkey>li-etal-2020-dice</bibkey>
       <pwccode url="https://github.com/ShannonAI/dice_loss_for_NLP" additional="true">ShannonAI/dice_loss_for_NLP</pwccode>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/msra-cn-ner">MSRA CN NER</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/ontonotes-4-0">OntoNotes 4.0</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/ontonotes-5-0">OntoNotes 5.0</pwcdataset>
@@ -1343,6 +1343,7 @@
       <video href="http://slideslive.com/38928951"/>
       <bibkey>miao-etal-2020-diverse</bibkey>
       <pwccode url="https://github.com/chaochun/nlu-asdiv-dataset" additional="false">chaochun/nlu-asdiv-dataset</pwccode>
+      <pwcdataset url="https://paperswithcode.com/dataset/asdiv">ASDiv</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/mathqa">MathQA</pwcdataset>
     </paper>
     <paper id="93">
@@ -2051,7 +2052,7 @@
       <video href="http://slideslive.com/38928783"/>
       <bibkey>namysl-etal-2020-nat</bibkey>
       <pwccode url="https://github.com/mnamysl/nat-acl2020" additional="false">mnamysl/nat-acl2020</pwccode>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
     </paper>
     <paper id="139">
       <title>Named Entity Recognition without Labelled Data: A Weak Supervision Approach</title>
@@ -2067,7 +2068,7 @@
       <bibkey>lison-etal-2020-named</bibkey>
       <pwccode url="https://github.com/NorskRegnesentral/weak-supervision-for-NER" additional="false">NorskRegnesentral/weak-supervision-for-NER</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/broad-twitter-corpus">Broad Twitter Corpus</pwcdataset>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
     </paper>
     <paper id="140">
       <title>Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction</title>
@@ -2772,7 +2773,7 @@
       <video href="http://slideslive.com/38929087"/>
       <bibkey>brantley-etal-2020-active</bibkey>
       <pwccode url="https://github.com/xkianteb/leaqi" additional="false">xkianteb/leaqi</pwccode>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
     </paper>
     <paper id="190">
       <title><fixed-case>E</fixed-case>xp<fixed-case>BERT</fixed-case>: Representation Engineering with Natural Language Explanations</title>
@@ -2814,7 +2815,7 @@
       <video href="http://slideslive.com/38928920"/>
       <bibkey>jiang-etal-2020-generalizing</bibkey>
       <pwccode url="https://github.com/jzbjyb/SpanRel" additional="true">jzbjyb/SpanRel</pwccode>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/conll-2012-1">CoNLL-2012</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/oie2016">OIE2016</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/penn-treebank">Penn Treebank</pwcdataset>
@@ -3490,7 +3491,7 @@
       <doi>10.18653/v1/2020.acl-main.236</doi>
       <video href="http://slideslive.com/38929148"/>
       <bibkey>arora-etal-2020-contextual</bibkey>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/glue">GLUE</pwcdataset>
     </paper>
     <paper id="237">
@@ -6575,7 +6576,7 @@
       <video href="http://slideslive.com/38929334"/>
       <bibkey>tabassum-etal-2020-code</bibkey>
       <pwccode url="https://github.com/jeniyat/StackOverflowNER" additional="true">jeniyat/StackOverflowNER</pwccode>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
     </paper>
     <paper id="444">
       <title>Dialogue-Based Relation Extraction</title>
@@ -7690,7 +7691,7 @@
       <pwccode url="https://github.com/ShannonAI/mrc-for-flat-nested-ner" additional="true">ShannonAI/mrc-for-flat-nested-ner</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/ace-2004">ACE 2004</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/ace-2005">ACE 2005</pwcdataset>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/genia">GENIA</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/msra-cn-ner">MSRA CN NER</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/ontonotes-4-0">OntoNotes 4.0</pwcdataset>
@@ -8198,7 +8199,6 @@
       <pwcdataset url="https://paperswithcode.com/dataset/reddit">Reddit</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/reddit-tifu">Reddit TIFU</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/wikihow">WikiHow</pwcdataset>
-      <pwcdataset url="https://paperswithcode.com/dataset/xsum">XSum</pwcdataset>
     </paper>
     <paper id="553">
       <title>Heterogeneous Graph Neural Networks for Extractive Document Summarization</title>
@@ -8528,7 +8528,7 @@
       <video href="http://slideslive.com/38928957"/>
       <bibkey>ouchi-etal-2020-instance</bibkey>
       <pwccode url="https://github.com/hiroki13/instance-based-ner" additional="false">hiroki13/instance-based-ner</pwccode>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
     </paper>
     <paper id="576">
       <title><fixed-case>MIE</fixed-case>: A Medical Information Extractor towards Medical Dialogues</title>
@@ -8562,7 +8562,7 @@
       <pwcdataset url="https://paperswithcode.com/dataset/ace-2004">ACE 2004</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/ace-2005">ACE 2005</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/conll-2002">CoNLL 2002</pwcdataset>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/genia">GENIA</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/ontonotes-5-0">OntoNotes 5.0</pwcdataset>
     </paper>
@@ -8624,7 +8624,7 @@
       <bibkey>wu-etal-2020-single</bibkey>
       <pwccode url="https://github.com/microsoft/vert-papers/tree/master/papers/SingleMulti-TS" additional="false">microsoft/vert-papers</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/conll-2002">CoNLL 2002</pwcdataset>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
     </paper>
     <paper id="582">
       <title>Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction</title>
@@ -10026,6 +10026,7 @@
       <video href="http://slideslive.com/38929348"/>
       <bibkey>wang-etal-2020-rat</bibkey>
       <pwccode url="https://github.com/Microsoft/rat-sql" additional="true">Microsoft/rat-sql</pwccode>
+      <pwcdataset url="https://paperswithcode.com/dataset/spider-realistic">Spider-Realistic</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/wikisql">WikiSQL</pwcdataset>
     </paper>
     <paper id="678">
@@ -10704,7 +10705,7 @@
       <video href="http://slideslive.com/38929323"/>
       <bibkey>rijhwani-etal-2020-soft</bibkey>
       <pwccode url="https://github.com/neulab/soft-gazetteers" additional="false">neulab/soft-gazetteers</pwccode>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
     </paper>
     <paper id="723">
       <title>A Prioritization Model for Suicidality Risk Assessment</title>
@@ -11033,6 +11034,7 @@
       <video href="http://slideslive.com/38929345"/>
       <bibkey>yin-etal-2020-tabert</bibkey>
       <pwccode url="https://github.com/facebookresearch/tabert" additional="false">facebookresearch/tabert</pwccode>
+      <pwcdataset url="https://paperswithcode.com/dataset/spider-realistic">Spider-Realistic</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/wikitablequestions">WikiTableQuestions</pwcdataset>
     </paper>
     <paper id="746">
@@ -11142,7 +11144,7 @@
       <bibkey>lin-etal-2020-triggerner</bibkey>
       <pwccode url="https://github.com/INK-USC/TriggerNER" additional="false">INK-USC/TriggerNER</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/bc5cdr">BC5CDR</pwcdataset>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
     </paper>
     <paper id="753">
       <title>Addressing Posterior Collapse with Mutual Information for Improved Variational Neural Machine Translation</title>
@@ -11582,7 +11584,7 @@
       <pwccode url="https://github.com/airaria/TextBrewer" additional="false">airaria/TextBrewer</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/cmrc">CMRC</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/cmrc-2018">CMRC 2018</pwcdataset>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/drcd">DRCD</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/squad">SQuAD</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/xnli">XNLI</pwcdataset>
@@ -11776,7 +11778,7 @@
       <video href="http://slideslive.com/38928620"/>
       <bibkey>qi-etal-2020-stanza</bibkey>
       <pwccode url="https://github.com/stanfordnlp/stanza" additional="true">stanfordnlp/stanza</pwccode>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
     </paper>
     <paper id="15">
       <title>jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models</title>

data/xml/2020.bionlp.xml

@@ -288,7 +288,6 @@
       <url hash="89a62594">2020.bionlp-1.19</url>
       <doi>10.18653/v1/2020.bionlp-1.19</doi>
       <bibkey>nejadgholi-etal-2020-extensive</bibkey>
-      <pwccode url="https://github.com/nrc-cnrc/NRC-MedNER-Eval" additional="false">nrc-cnrc/NRC-MedNER-Eval</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/medmentions">MedMentions</pwcdataset>
     </paper>
     <paper id="20">

data/xml/2020.coling.xml

@@ -69,7 +69,7 @@
       <bibkey>ma-etal-2020-charbert</bibkey>
       <pwccode url="https://github.com/wtma/CharBERT" additional="false">wtma/CharBERT</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/cola">CoLA</pwcdataset>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</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/qnli">QNLI</pwcdataset>
@@ -484,7 +484,7 @@
       <url hash="3fabc411">2020.coling-main.36</url>
       <doi>10.18653/v1/2020.coling-main.36</doi>
       <bibkey>ashby-weir-2020-leveraging</bibkey>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
     </paper>
     <paper id="37">
       <title>Multimodal Review Generation with Privacy and Fairness Awareness</title>
@@ -1047,7 +1047,7 @@
       <bibkey>luoma-pyysalo-2020-exploring</bibkey>
       <pwccode url="https://github.com/jouniluoma/bert-ner-cmv" additional="false">jouniluoma/bert-ner-cmv</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/conll-2002">CoNLL 2002</pwcdataset>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
     </paper>
     <paper id="79">
       <title>Cross-lingual Annotation Projection in Legal Texts</title>
@@ -4375,6 +4375,7 @@
       <pwcdataset url="https://paperswithcode.com/dataset/glue">GLUE</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/lama">LAMA</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/open-entity-1">Open Entity</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/wikidata5m">Wikidata5M</pwcdataset>
     </paper>
     <paper id="328">
       <title>Invertible Tree Embeddings using a Cryptographic Role Embedding Scheme</title>
@@ -5217,7 +5218,6 @@
       <url hash="d02dc5d3">2020.coling-main.392</url>
       <doi>10.18653/v1/2020.coling-main.392</doi>
       <bibkey>zhang-etal-2020-knowledge</bibkey>
-      <pwccode url="https://github.com/BladeDancer957/KAITML" additional="false">BladeDancer957/KAITML</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/conceptnet">ConceptNet</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/emorynlp">EmoryNLP</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/iemocap">IEMOCAP</pwcdataset>
@@ -6126,7 +6126,7 @@
       <url hash="8845c18d">2020.coling-main.460</url>
       <doi>10.18653/v1/2020.coling-main.460</doi>
       <bibkey>vo-etal-2020-identifying</bibkey>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sick">SICK</pwcdataset>
     </paper>
     <paper id="461">
@@ -7317,7 +7317,7 @@
       <url hash="c9601ff6">2020.coling-main.550</url>
       <doi>10.18653/v1/2020.coling-main.550</doi>
       <bibkey>suwaileh-etal-2020-ready</bibkey>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
     </paper>
     <paper id="551">
       <title>Mining Crowdsourcing Problems from Discussion Forums of Workers</title>

data/xml/2020.conll.xml

@@ -222,8 +222,8 @@
       <doi>10.18653/v1/2020.conll-1.16</doi>
       <bibkey>reiss-etal-2020-identifying</bibkey>
       <pwccode url="https://github.com/codait/text-extensions-for-pandas" additional="true">codait/text-extensions-for-pandas</pwccode>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/conll">CoNLL++</pwcdataset>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/rcv1">RCV1</pwcdataset>
     </paper>
     <paper id="17">
@@ -459,7 +459,7 @@
       <doi>10.18653/v1/2020.conll-1.35</doi>
       <bibkey>boros-etal-2020-alleviating</bibkey>
       <pwccode url="https://github.com/embeddia/stacked-ner" additional="false">embeddia/stacked-ner</pwccode>
-      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL-2003</pwcdataset>
+      <pwcdataset url="https://paperswithcode.com/dataset/conll-2003">CoNLL 2003</pwcdataset>
     </paper>
     <paper id="36">
       <title>Analysing Word Representation from the Input and Output Embeddings in Neural Network Language Models</title>