added EACL 2023 video presentations

data/xml/2021.tacl.xml

@@ -823,6 +823,7 @@
       <pages>1061–1080</pages>
       <url hash="bf604c27">2021.tacl-1.63</url>
       <bibkey>ganesh-etal-2021-compressing</bibkey>
+      <video href="2021.tacl-1.63.mp4"/>
     </paper>
     <paper id="64">
       <title>He Thinks He Knows Better than the Doctors: <fixed-case>BERT</fixed-case> for Event Factuality Fails on Pragmatics</title>
@@ -1033,6 +1034,7 @@
       <pages>1249–1267</pages>
       <url hash="257f7dfa">2021.tacl-1.74</url>
       <bibkey>czarnowska-etal-2021-quantifying</bibkey>
+      <video href="2021.tacl-1.74.mp4"/>
     </paper>
     <paper id="75">
       <title>Structured Self-Supervised Pretraining for Commonsense Knowledge Graph Completion</title>
@@ -1125,6 +1127,7 @@
       <pages>1374–1388</pages>
       <url hash="e8241bc3">2021.tacl-1.81</url>
       <bibkey>mitropolsky-etal-2021-biologically</bibkey>
+      <video href="2021.tacl-1.81.mp4"/>
     </paper>
     <paper id="82">
       <title><fixed-case>MKQA</fixed-case>: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering</title>
@@ -1163,6 +1166,7 @@
       <pages>1408–1424</pages>
       <url hash="4cd1ab6c">2021.tacl-1.84</url>
       <bibkey>schick-etal-2021-self</bibkey>
+      <video href="2021.tacl-1.84.mp4"/>
     </paper>
     <paper id="85">
       <title>Weisfeiler-Leman in the Bamboo: Novel <fixed-case>AMR</fixed-case> Graph Metrics and a Benchmark for <fixed-case>AMR</fixed-case> Graph Similarity</title>
@@ -1200,6 +1204,7 @@
       <pages>1460–1474</pages>
       <url hash="217e36e6">2021.tacl-1.87</url>
       <bibkey>freitag-etal-2021-experts</bibkey>
+      <video href="2021.tacl-1.87.mp4"/>
     </paper>
     <paper id="88">
       <title>Planning with Learned Entity Prompts for Abstractive Summarization</title>
@@ -1214,6 +1219,7 @@
       <pages>1475–1492</pages>
       <url hash="53abf69e">2021.tacl-1.88</url>
       <bibkey>narayan-etal-2021-planning</bibkey>
+      <video href="2021.tacl-1.88.mp4"/>
     </paper>
     <paper id="89">
       <title>Instance-Based Neural Dependency Parsing</title>
@@ -1253,6 +1259,7 @@
       <pages>1529–1545</pages>
       <url hash="43d162e6">2021.tacl-1.91</url>
       <bibkey>francis-etal-2021-quantifying</bibkey>
+      <video href="2021.tacl-1.91.mp4"/>
     </paper>
     <paper id="92">
       <title>Idiomatic Expression Identification using Semantic Compatibility</title>
@@ -1274,6 +1281,7 @@
       <pages>1563–1579</pages>
       <url hash="0c3c9742">2021.tacl-1.93</url>
       <bibkey>pezzelle-etal-2021-word</bibkey>
+      <video href="2021.tacl-1.93.mp4"/>
     </paper>
   </volume>
 </collection>

data/xml/2022.findings.xml

@@ -8786,6 +8786,7 @@
       <abstract>In light of unprecedented increases in the popularity of the internet and social media, comment moderation has never been a more relevant task. Semi-automated comment moderation systems greatly aid human moderators by either automatically classifying the examples or allowing the moderators to prioritize which comments to consider first. However, the concept of inappropriate content is often subjective, and such content can be conveyed in many subtle and indirect ways. In this work, we propose CoRAL – a language and culturally aware Croatian Abusive dataset covering phenomena of implicitness and reliance on local and global context. We show experimentally that current models degrade when comments are not explicit and further degrade when language skill and context knowledge are required to interpret the comment.</abstract>
       <url hash="2e94e643">2022.findings-aacl.21</url>
       <bibkey>shekhar-etal-2022-coral</bibkey>
+      <video href="2022.findings-aacl.21.mp4"/>
     </paper>
     <paper id="22">
       <title>A Copy Mechanism for Handling Knowledge Base Elements in <fixed-case>SPARQL</fixed-case> Neural Machine Translation</title>

data/xml/2022.tacl.xml

@@ -19,6 +19,7 @@
       <pages>1–16</pages>
       <url hash="49b3ffae">2022.tacl-1.1</url>
       <bibkey>chang-bergen-2022-word</bibkey>
+      <video href="2022.tacl-1.1.mp4"/>
     </paper>
     <paper id="2">
       <title>Decomposing and Recomposing Event Structure</title>
@@ -116,6 +117,7 @@
       <pages>50–72</pages>
       <url hash="db342649">2022.tacl-1.4</url>
       <bibkey>kreutzer-etal-2022-quality</bibkey>
+      <video href="2022.tacl-1.4.mp4"/>
     </paper>
     <paper id="5">
       <title>Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation</title>
@@ -128,6 +130,7 @@
       <pages>73–91</pages>
       <url hash="0de3f9e2">2022.tacl-1.5</url>
       <bibkey>clark-etal-2022-canine</bibkey>
+      <video href="2022.tacl-1.5.mp4"/>
     </paper>
     <paper id="6">
       <title>Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations</title>
@@ -139,6 +142,7 @@
       <pages>92–110</pages>
       <url hash="ab44fbfa">2022.tacl-1.6</url>
       <bibkey>davani-etal-2022-dealing</bibkey>
+      <video href="2022.tacl-1.6.mp4"/>
     </paper>
     <paper id="7">
       <title>Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question Decomposition</title>
@@ -150,6 +154,7 @@
       <pages>111–126</pages>
       <url hash="13f13e39">2022.tacl-1.7</url>
       <bibkey>geva-etal-2022-break</bibkey>
+      <video href="2022.tacl-1.7.mp4"/>
     </paper>
     <paper id="8">
       <title>Out-of-Domain Discourse Dependency Parsing via Bootstrapping: An Empirical Analysis on Its Effectiveness and Limitation</title>
@@ -160,6 +165,7 @@
       <pages>127–144</pages>
       <url hash="9822021e">2022.tacl-1.8</url>
       <bibkey>nishida-matsumoto-2022-domain</bibkey>
+      <video href="2022.tacl-1.8.mp4"/>
     </paper>
     <paper id="9">
       <title>Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 <fixed-case>I</fixed-case>ndic Languages</title>
@@ -186,6 +192,7 @@
       <pages>145–162</pages>
       <url hash="87b5fb09">2022.tacl-1.9</url>
       <bibkey>ramesh-etal-2022-samanantar</bibkey>
+      <video href="2022.tacl-1.9.mp4"/>
     </paper>
     <paper id="10">
       <title><fixed-case>S</fixed-case>umma<fixed-case>C</fixed-case>: Re-Visiting <fixed-case>NLI</fixed-case>-based Models for Inconsistency Detection in Summarization</title>
@@ -198,6 +205,7 @@
       <pages>163–177</pages>
       <url hash="868cc089">2022.tacl-1.10</url>
       <bibkey>laban-etal-2022-summac</bibkey>
+      <video href="2022.tacl-1.10.mp4"/>
     </paper>
     <paper id="11">
       <title>A Survey on Automated Fact-Checking</title>
@@ -209,6 +217,7 @@
       <pages>178–206</pages>
       <url hash="9bd6cc19">2022.tacl-1.11</url>
       <bibkey>guo-etal-2022-survey</bibkey>
+      <video href="2022.tacl-1.11.mp4"/>
     </paper>
     <paper id="12">
       <title>Predicting Document Coverage for Relation Extraction</title>
@@ -220,6 +229,7 @@
       <pages>207–223</pages>
       <url hash="bf658a66">2022.tacl-1.12</url>
       <bibkey>singhania-etal-2022-predicting</bibkey>
+      <video href="2022.tacl-1.12.mp4"/>
     </paper>
     <paper id="13">
       <title><fixed-case>ABNIRML</fixed-case>: Analyzing the Behavior of Neural <fixed-case>IR</fixed-case> Models</title>
@@ -233,6 +243,7 @@
       <pages>224–239</pages>
       <url hash="4c991d03">2022.tacl-1.13</url>
       <bibkey>macavaney-etal-2022-abnirml</bibkey>
+      <video href="2022.tacl-1.13.mp4"/>
     </paper>
     <paper id="14">
       <title>Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference</title>
@@ -259,6 +270,7 @@
       <pages>257–273</pages>
       <url hash="fa836992">2022.tacl-1.15</url>
       <bibkey>dhingra-etal-2022-time</bibkey>
+      <video href="2022.tacl-1.15.mp4"/>
     </paper>
     <paper id="16">
       <title>Multilingual Autoregressive Entity Linking</title>
@@ -277,6 +289,7 @@
       <pages>274–290</pages>
       <url hash="376b497c">2022.tacl-1.16</url>
       <bibkey>de-cao-etal-2022-multilingual</bibkey>
+      <video href="2022.tacl-1.16.mp4"/>
     </paper>
     <paper id="17">
       <title><fixed-case>B</fixed-case>y<fixed-case>T</fixed-case>5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models</title>
@@ -293,6 +306,7 @@
       <pages>291–306</pages>
       <url hash="3db6b62d">2022.tacl-1.17</url>
       <bibkey>xue-etal-2022-byt5</bibkey>
+      <video href="2022.tacl-1.17.mp4"/>
     </paper>
     <paper id="18">
       <title>Designing an Automatic Agent for Repeated Language–based Persuasion Games</title>
@@ -316,6 +330,7 @@
       <pages>325–342</pages>
       <url hash="67da10c5">2022.tacl-1.19</url>
       <bibkey>saparov-mitchell-2022-towards</bibkey>
+      <video href="2022.tacl-1.19.mp4"/>
     </paper>
     <paper id="20">
       <title>A Multi-Level Optimization Framework for End-to-End Text Augmentation</title>
@@ -344,6 +359,7 @@
       <pages>359–375</pages>
       <url hash="85ed9ebf">2022.tacl-1.21</url>
       <bibkey>pruthi-etal-2022-evaluating</bibkey>
+      <video href="2022.tacl-1.21.mp4"/>
     </paper>
     <paper id="22">
       <title><fixed-case>VILA</fixed-case>: Improving Structured Content Extraction from Scientific <fixed-case>PDF</fixed-case>s Using Visual Layout Groups</title>
@@ -358,6 +374,7 @@
       <pages>376–392</pages>
       <url hash="7766e7cf">2022.tacl-1.22</url>
       <bibkey>shen-etal-2022-vila</bibkey>
+      <video href="2022.tacl-1.22.mp4"/>
     </paper>
     <paper id="23">
       <title>Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation</title>
@@ -380,6 +397,7 @@
       <pages>414–433</pages>
       <url hash="755969f8">2022.tacl-1.24</url>
       <bibkey>ben-david-etal-2022-pada</bibkey>
+      <video href="2022.tacl-1.24.mp4"/>
     </paper>
     <paper id="25">
       <title><fixed-case>LOT</fixed-case>: A Story-Centric Benchmark for Evaluating <fixed-case>C</fixed-case>hinese Long Text Understanding and Generation</title>
@@ -421,6 +439,7 @@
       <pages>468–483</pages>
       <url hash="78eee5a1">2022.tacl-1.27</url>
       <bibkey>adlakha-etal-2022-topiocqa</bibkey>
+      <video href="2022.tacl-1.27.mp4"/>
     </paper>
     <paper id="28">
       <title>A Neighborhood Framework for Resource-Lean Content Flagging</title>
@@ -448,6 +467,7 @@
       <pages>503–521</pages>
       <url hash="531eb1e6">2022.tacl-1.29</url>
       <bibkey>geigle-etal-2022-retrieve</bibkey>
+      <video href="2022.tacl-1.29.mp4"/>
     </paper>
     <paper id="30">
       <title>The <fixed-case>F</fixed-case>lores-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation</title>
@@ -466,6 +486,7 @@
       <pages>522–538</pages>
       <url hash="68f79ffa">2022.tacl-1.30</url>
       <bibkey>goyal-etal-2022-flores</bibkey>
+      <video href="2022.tacl-1.30.mp4"/>
     </paper>
     <paper id="31">
       <title>♫ <fixed-case>M</fixed-case>u<fixed-case>S</fixed-case>i<fixed-case>Q</fixed-case>ue: Multihop Questions via Single-hop Question Composition</title>
@@ -516,6 +537,7 @@
       <pages>589–606</pages>
       <url hash="4737650b">2022.tacl-1.34</url>
       <bibkey>chakrabarty-etal-2022-rocket</bibkey>
+      <video href="2022.tacl-1.34.mp4"/>
     </paper>
     <paper id="35">
       <title>Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference</title>
@@ -538,6 +560,7 @@
       <pages>623–638</pages>
       <url hash="c13652c6">2022.tacl-1.36</url>
       <bibkey>xu-lapata-2022-document</bibkey>
+      <video href="2022.tacl-1.36.mp4"/>
     </paper>
     <paper id="37">
       <title>End-to-end Argument Mining with Cross-corpora Multi-task Learning</title>
@@ -564,6 +587,7 @@
       <pages>659–679</pages>
       <url hash="a4f5040f">2022.tacl-1.38</url>
       <bibkey>gupta-etal-2022-model</bibkey>
+      <video href="2022.tacl-1.38.mp4"/>
     </paper>
     <paper id="39">
       <title>Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression</title>
@@ -721,6 +745,7 @@
       <pages>873–887</pages>
       <url hash="a29c0f0a">2022.tacl-1.51</url>
       <bibkey>osborne-etal-2022-survey</bibkey>
+      <video href="2022.tacl-1.51.mp4"/>
     </paper>
     <paper id="52">
       <title>Dependency Parsing with Backtracking using Deep Reinforcement Learning</title>
@@ -787,6 +812,7 @@
       <pages>981–996</pages>
       <url hash="57f74758">2022.tacl-1.57</url>
       <bibkey>mickus-etal-2022-dissect</bibkey>
+      <video href="2022.tacl-1.57.mp4"/>
     </paper>
     <paper id="58">
       <title>On Decoding Strategies for Neural Text Generators</title>