A Two-Step Approach for Implicit Event Argument Detection

Zhisong Zhang, Xiang Kong, Zhengzhong Liu, Xuezhe Ma, Eduard Hovy


Abstract
In this work, we explore the implicit event argument detection task, which studies event arguments beyond sentence boundaries. The addition of cross-sentence argument candidates imposes great challenges for modeling. To reduce the number of candidates, we adopt a two-step approach, decomposing the problem into two sub-problems: argument head-word detection and head-to-span expansion. Evaluated on the recent RAMS dataset (Ebner et al., 2020), our model achieves overall better performance than a strong sequence labeling baseline. We further provide detailed error analysis, presenting where the model mainly makes errors and indicating directions for future improvements. It remains a challenge to detect implicit arguments, calling for more future work of document-level modeling for this task.
Anthology ID:
2020.acl-main.667
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7479–7485
Language:
URL:
https://aclanthology.org/2020.acl-main.667
DOI:
10.18653/v1/2020.acl-main.667
Bibkey:
Cite (ACL):
Zhisong Zhang, Xiang Kong, Zhengzhong Liu, Xuezhe Ma, and Eduard Hovy. 2020. A Two-Step Approach for Implicit Event Argument Detection. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7479–7485, Online. Association for Computational Linguistics.
Cite (Informal):
A Two-Step Approach for Implicit Event Argument Detection (Zhang et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.667.pdf
Video:
 http://slideslive.com/38928918