Document-Level Event Argument Extraction via Optimal Transport

Amir Pouran Ben Veyseh, Minh Van Nguyen, Franck Dernoncourt, Bonan Min, Thien Nguyen


Abstract
Event Argument Extraction (EAE) is one of the sub-tasks of event extraction, aiming to recognize the role of each entity mention toward a specific event trigger. Despite the success of prior works in sentence-level EAE, the document-level setting is less explored. In particular, whereas syntactic structures of sentences have been shown to be effective for sentence-level EAE, prior document-level EAE models totally ignore syntactic structures for documents. Hence, in this work, we study the importance of syntactic structures in document-level EAE. Specifically, we propose to employ Optimal Transport (OT) to induce structures of documents based on sentence-level syntactic structures and tailored to EAE task. Furthermore, we propose a novel regularization technique to explicitly constrain the contributions of unrelated context words in the final prediction for EAE. We perform extensive experiments on the benchmark document-level EAE dataset RAMS that leads to the state-of-the-art performance. Moreover, our experiments on the ACE 2005 dataset reveals the effectiveness of the proposed model in the sentence-level EAE by establishing new state-of-the-art results.
Anthology ID:
2022.findings-acl.130
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1648–1658
Language:
URL:
https://aclanthology.org/2022.findings-acl.130
DOI:
10.18653/v1/2022.findings-acl.130
Bibkey:
Cite (ACL):
Amir Pouran Ben Veyseh, Minh Van Nguyen, Franck Dernoncourt, Bonan Min, and Thien Nguyen. 2022. Document-Level Event Argument Extraction via Optimal Transport. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1648–1658, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Document-Level Event Argument Extraction via Optimal Transport (Pouran Ben Veyseh et al., Findings 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/add_acl24_videos/2022.findings-acl.130.pdf
Software:
 2022.findings-acl.130.software.zip