Update metadata from Papers with Code

data/xml/2020.aacl.xml

@@ -1553,7 +1553,7 @@
       <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" additional="true">pytorch/fairseq</pwccode>
+      <pwccode url="https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text" additional="true">pytorch/fairseq</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/covost2">CoVoST2</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/librispeech">LibriSpeech</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/must-c">MuST-C</pwcdataset>

data/xml/2020.emnlp.xml

@@ -7532,7 +7532,7 @@
       <doi>10.18653/v1/2020.emnlp-main.498</doi>
       <video href="https://slideslive.com/38938695"/>
       <bibkey>garg-ramakrishnan-2020-bae</bibkey>
-      <pwccode url="https://github.com/QData/TextAttack" additional="true">QData/TextAttack</pwccode>
+      <pwccode url="https://github.com/QData/TextAttack/blob/master/textattack/attack_recipes/bae_garg_2019.py" additional="true">QData/TextAttack</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/imdb-binary">IMDB-BINARY</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/mpqa-opinion-corpus">MPQA Opinion Corpus</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/mr">MR</pwcdataset>

data/xml/2020.findings.xml

@@ -2548,7 +2548,6 @@
       <pwcdataset url="https://paperswithcode.com/dataset/commonsenseqa">CommonsenseQA</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/drop">DROP</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/mctest">MCTest</pwcdataset>
-      <pwcdataset url="https://paperswithcode.com/dataset/mmlu">MMLU</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/multirc">MultiRC</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/narrativeqa">NarrativeQA</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/newsqa">NewsQA</pwcdataset>

data/xml/2022.bigscience.xml

@@ -190,7 +190,6 @@
       <pwcdataset url="https://paperswithcode.com/dataset/lambada">LAMBADA</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/logiqa">LogiQA</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/math">MATH</pwcdataset>
-      <pwcdataset url="https://paperswithcode.com/dataset/mmlu">MMLU</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/piqa">PIQA</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/prost">PROST</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/the-pile">The Pile</pwcdataset>

data/xml/D14.xml

@@ -1930,7 +1930,6 @@
       <pwccode url="" additional="true"/>
       <pwcdataset url="https://paperswithcode.com/dataset/cped">CPED</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/mpqa-opinion-corpus">MPQA Opinion Corpus</pwcdataset>
-      <pwcdataset url="https://paperswithcode.com/dataset/snli">SNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sst">SST</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sst-2">SST-2</pwcdataset>
     </paper>

data/xml/P14.xml

@@ -755,7 +755,6 @@
       <mrf src="latexml">P14-1062.xhtml</mrf>
       <bibkey>kalchbrenner-etal-2014-convolutional</bibkey>
       <pwccode url="" additional="true"/>
-      <pwcdataset url="https://paperswithcode.com/dataset/snli">SNLI</pwcdataset>
     </paper>
     <paper id="63">
       <title>Online Learning in Tensor Space</title>

data/xml/P15.xml

@@ -1684,7 +1684,6 @@
       <bibkey>tai-etal-2015-improved</bibkey>
       <pwccode url="https://github.com/stanfordnlp/treelstm" additional="true">stanfordnlp/treelstm</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/sick">SICK</pwcdataset>
-      <pwcdataset url="https://paperswithcode.com/dataset/snli">SNLI</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sst">SST</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sst-2">SST-2</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/sst-5">SST-5</pwcdataset>

data/xml/W19.xml

@@ -2944,7 +2944,7 @@
       <url hash="bbc801a2">W19-2304</url>
       <doi>10.18653/v1/W19-2304</doi>
       <bibkey>wang-cho-2019-bert</bibkey>
-      <pwccode url="https://github.com/kyunghyuncho/bert-gen" additional="true">kyunghyuncho/bert-gen</pwccode>
+      <pwccode url="https://github.com/nyu-dl/bert-gen" additional="true">nyu-dl/bert-gen</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/bookcorpus">BookCorpus</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/wikitext-103">WikiText-103</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/wikitext-2">WikiText-2</pwcdataset>