Revision: 20411
          http://sourceforge.net/p/gate/code/20411
Author:   gate-project
Date:     2024-10-15 11:01:19 +0000 (Tue, 15 Oct 2024)
Log Message:
-----------
[diana] Edited with GATEWiki

Modified Paths:
--------------
    gate/trunk/doc/papers.html
    gate/trunk/doc/papers.yam

Modified: gate/trunk/doc/papers.html
===================================================================
--- gate/trunk/doc/papers.html  2024-10-14 09:26:49 UTC (rev 20410)
+++ gate/trunk/doc/papers.html  2024-10-15 11:01:19 UTC (rev 20411)
@@ -126,11 +126,11 @@
 <ul>
 <li>Yue Li and Carolina Scarton. 2024. Can We Identify Stance without Target 
Arguments? A Study for Rumour Stance Classification. In Proceedings of the 2024 
Joint International Conference on Computational Linguistics, Language Resources 
and Evaluation (LREC-COLING 2024), pages 2844&ndash;2851, Torino, Italia. ELRA 
and ICCL. <a class="cow-url" 
href="https://aclanthology.org/2024.lrec-main.253/";>https://aclanthology.org/2024.lrec-main.253/</a></li>
 </ul>
-<h3 class="cow-heading">Pre-print</h3>
 
 <ul>
-<li>W. Thorne, A. Robinson, B. Peng, C. Lin, D. Maynard (2024) Increasing the 
Difficulty of Automatically Generated Questions via Reinforcement Learning with 
Synthetic Preference. <a class="cow-url" href="arXiv preprint 
https:/arxiv.org/pdf/2410.08289">arXiv preprint 
https://arxiv.org/pdf/2410.08289</a></li>
+<li>W. Thorne, A. Robinson, B. Peng, C. Lin, D. Maynard (2024) Increasing the 
Difficulty of Automatically Generated Questions via Reinforcement Learning with 
Synthetic Preference. Proc oftThe 4th International Conference on Natural 
Language Processing for Digital Humanities &ndash; NLP4DH 2024. <a 
class="cow-url" href="arXiv preprint https:/arxiv.org/pdf/2410.08289">arXiv 
preprint https://arxiv.org/pdf/2410.08289</a></li>
 </ul>
+<h3 class="cow-heading">Pre-print</h3>
 
 <ul>
 <li>Leite, J. A., Razuvayevskaya, O., Bontcheva, K., &amp; Scarton, C. (2024) 
EUvsDisinfo: a Dataset for Multilingual Detection of Pro-Kremlin Disinformation 
in News Articles <a class="cow-url" href="arXiv preprint 
arXiv:2406.12614">arXiv preprint arXiv:2406.12614</a></li>

Modified: gate/trunk/doc/papers.yam
===================================================================
--- gate/trunk/doc/papers.yam   2024-10-14 09:26:49 UTC (rev 20410)
+++ gate/trunk/doc/papers.yam   2024-10-15 11:01:19 UTC (rev 20411)
@@ -63,10 +63,10 @@
 
 - Yue Li and Carolina Scarton. 2024. Can We Identify Stance without Target 
Arguments? A Study for Rumour Stance Classification. In Proceedings of the 2024 
Joint International Conference on Computational Linguistics, Language Resources 
and Evaluation (LREC-COLING 2024), pages 2844–2851, Torino, Italia. ELRA and 
ICCL. %(https://aclanthology.org/2024.lrec-main.253/)
 
+- W. Thorne, A. Robinson, B. Peng, C. Lin, D. Maynard (2024) Increasing the 
Difficulty of Automatically Generated Questions via Reinforcement Learning with 
Synthetic Preference. Proc oftThe 4th International Conference on Natural 
Language Processing for Digital Humanities – NLP4DH 2024. %(arXiv preprint 
https://arxiv.org/pdf/2410.08289)
+
 %3* Pre-print
 
-- W. Thorne, A. Robinson, B. Peng, C. Lin, D. Maynard (2024) Increasing the 
Difficulty of Automatically Generated Questions via Reinforcement Learning with 
Synthetic Preference. %(arXiv preprint https://arxiv.org/pdf/2410.08289)
-
 - Leite, J. A., Razuvayevskaya, O., Bontcheva, K., & Scarton, C. (2024) 
EUvsDisinfo: a Dataset for Multilingual Detection of Pro-Kremlin Disinformation 
in News Articles %(arXiv preprint arXiv:2406.12614)
 
 - Freddy Heppell, Mehmet E. Bakir, Kalina Bontcheva (2024) Lying Blindly: 
Bypassing ChatGPT's Safeguards to Generate Hard-to-Detect Disinformation Claims 
at Scale %(https://arxiv.org/abs/2402.08467)

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