Paper Revision: {2024.clasp-1.8}, closes #3958.

data/xml/2024.clasp.xml

@@ -96,8 +96,10 @@
       <author><first>Bill</first><last>Noble</last></author>
       <pages>56–61</pages>
       <abstract>This paper outlines the ongoing research project “Not Just Semantics: Word Meaning Negotiation in Social Media and Spoken Interaction”. The goal of the project is to investigate how meanings of words (and phrases) are interactively negotiated in social media and in spoken interaction. This project will contribute towards a comprehensive theory of word meaning negotiation.</abstract>
-      <url hash="005731a5">2024.clasp-1.8</url>
+      <url hash="9c43448d">2024.clasp-1.8</url>
       <bibkey>larsson-etal-2024-just</bibkey>
+      <revision id="1" href="2024.clasp-1.8v1" hash="005731a5"/>
+      <revision id="2" href="2024.clasp-1.8v2" hash="9c43448d" date="2024-10-18">Minor update.</revision>
     </paper>
     <paper id="9">
       <title>Toward Real Time Word Based Prosody Recognition</title>

data/xml/2024.findings.xml

@@ -13590,9 +13590,11 @@
       <author><first>Wenyu</first><last>Chen</last></author>
       <pages>9470-9487</pages>
       <abstract>Recent mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring the correlations among multiple events. To address these limitations, here we propose a multiple-event argument extraction model DEEIA (Dependency-guided Encoding and Event-specific Information Aggregation), capable of extracting arguments from all events within a document simultaneously. The proposed DEEIA model employs a multi-event prompt mechanism, comprising DE and EIA modules. The DE module is designed to improve the correlation between prompts and their corresponding event contexts, whereas the EIA module provides event-specific information to improve contextual understanding. Extensive experiments show that our method achieves new state-of-the-art performance on four public datasets (RAMS, WikiEvents, MLEE, and ACE05), while significantly saving the inference time compared to the baselines. Further analyses demonstrate the effectiveness of the proposed modules.</abstract>
-      <url hash="e27958ff">2024.findings-acl.564</url>
+      <url hash="2b8fb127">2024.findings-acl.564</url>
       <bibkey>liu-etal-2024-beyond-single</bibkey>
       <doi>10.18653/v1/2024.findings-acl.564</doi>
+      <revision id="1" href="2024.findings-acl.564v1" hash="e27958ff"/>
+      <revision id="2" href="2024.findings-acl.564v2" hash="2b8fb127" date="2024-10-18">Paper Revision: {2024.findings-acl.564}, closes #3957.</revision>
     </paper>
     <paper id="565">
       <title>Revisiting Interpolation Augmentation for Speech-to-Text Generation</title>