From f271406e0052fc4019629f36ec7dc268f3e7efac Mon Sep 17 00:00:00 2001
From: acl-pwc-bot <acl-pwc-bot@bollmann.me>
Date: Thu, 26 Sep 2024 00:48:32 +0000
Subject: [PATCH] Update metadata from Papers with Code

---
 data/xml/2020.acl.xml    | 2 +-
 data/xml/2020.coling.xml | 1 +
 data/xml/2022.acl.xml    | 1 -
 data/xml/2022.lrec.xml   | 2 +-
 data/xml/2022.naacl.xml  | 1 +
 5 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/data/xml/2020.acl.xml b/data/xml/2020.acl.xml
index 9494d3cb5e..505e311c55 100644
--- a/data/xml/2020.acl.xml
+++ b/data/xml/2020.acl.xml
@@ -2507,7 +2507,7 @@
       <doi>10.18653/v1/2020.acl-main.169</doi>
       <video href="http://slideslive.com/38929267"/>
       <bibkey>madaan-etal-2020-politeness</bibkey>
-      <pwccode url="https://github.com/tag-and-generate/Politeness-Transfer-A-Tag-and-Generate-Approach" additional="true">tag-and-generate/Politeness-Transfer-A-Tag-and-Generate-Approach</pwccode>
+      <pwccode url="https://github.com/tag-and-generate/Politeness-Transfer-A-Tag-and-Generate-Approach" additional="false">tag-and-generate/Politeness-Transfer-A-Tag-and-Generate-Approach</pwccode>
     </paper>
     <paper id="170">
       <title><fixed-case>BPE</fixed-case>-Dropout: Simple and Effective Subword Regularization</title>
diff --git a/data/xml/2020.coling.xml b/data/xml/2020.coling.xml
index 2cd29553c7..f19a05cc4e 100644
--- a/data/xml/2020.coling.xml
+++ b/data/xml/2020.coling.xml
@@ -6944,6 +6944,7 @@
       <url hash="853d8af4">2020.coling-main.519</url>
       <doi>10.18653/v1/2020.coling-main.519</doi>
       <bibkey>clifton-etal-2020-100000</bibkey>
+      <pwcdataset url="https://paperswithcode.com/dataset/urban-hyperspectral-image">Urban Hyperspectral Image</pwcdataset>
     </paper>
     <paper id="520">
       <title>A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment</title>
diff --git a/data/xml/2022.acl.xml b/data/xml/2022.acl.xml
index cee8ceb0ef..6f8a45725d 100644
--- a/data/xml/2022.acl.xml
+++ b/data/xml/2022.acl.xml
@@ -9167,7 +9167,6 @@ in the Case of Unambiguous Gender</title>
       <bibkey>vazhentsev-etal-2022-uncertainty</bibkey>
       <doi>10.18653/v1/2022.acl-long.566</doi>
       <video href="2022.acl-long.566.mp4"/>
-      <pwccode url="https://github.com/airi-institute/uncertainty_transformers" additional="false">airi-institute/uncertainty_transformers</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/glue">GLUE</pwcdataset>
diff --git a/data/xml/2022.lrec.xml b/data/xml/2022.lrec.xml
index 1f0716e036..8541934e8b 100644
--- a/data/xml/2022.lrec.xml
+++ b/data/xml/2022.lrec.xml
@@ -5685,7 +5685,7 @@
       <abstract>In recent years there have been considerable advances in pre-trained language models, where non-English language versions have also been made available. Due to their increasing use, many lightweight versions of these models (with reduced parameters) have also been released to speed up training and inference times. However, versions of these lighter models (e.g., ALBERT, DistilBERT) for languages other than English are still scarce. In this paper we present ALBETO and DistilBETO, which are versions of ALBERT and DistilBERT pre-trained exclusively on Spanish corpora. We train several versions of ALBETO ranging from 5M to 223M parameters and one of DistilBETO with 67M parameters. We evaluate our models in the GLUES benchmark that includes various natural language understanding tasks in Spanish. The results show that our lightweight models achieve competitive results to those of BETO (Spanish-BERT) despite having fewer parameters. More specifically, our larger ALBETO model outperforms all other models on the MLDoc, PAWS-X, XNLI, MLQA, SQAC and XQuAD datasets. However, BETO remains unbeaten for POS and NER. As a further contribution, all models are publicly available to the community for future research.</abstract>
       <url hash="f2258b34">2022.lrec-1.457</url>
       <bibkey>canete-etal-2022-albeto</bibkey>
-      <pwccode url="https://github.com/dccuchile/lightweight-spanish-language-models" additional="true">dccuchile/lightweight-spanish-language-models</pwccode>
+      <pwccode url="https://github.com/OpenCENIA/lightweight-spanish-language-models" additional="true">OpenCENIA/lightweight-spanish-language-models</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/conll-2002">CoNLL 2002</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/mldoc">MLDoc</pwcdataset>
       <pwcdataset url="https://paperswithcode.com/dataset/mlqa">MLQA</pwcdataset>
diff --git a/data/xml/2022.naacl.xml b/data/xml/2022.naacl.xml
index e45130bf39..8ff3b892dc 100644
--- a/data/xml/2022.naacl.xml
+++ b/data/xml/2022.naacl.xml
@@ -5739,6 +5739,7 @@
       <bibkey>zhao-etal-2022-epida</bibkey>
       <doi>10.18653/v1/2022.naacl-main.349</doi>
       <video href="2022.naacl-main.349.mp4"/>
+      <pwccode url="https://github.com/zhaominyiz/epida" additional="false">zhaominyiz/epida</pwccode>
       <pwcdataset url="https://paperswithcode.com/dataset/ag-news">AG News</pwcdataset>
     </paper>
     <paper id="350">