CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction

Yubing Ren, Yanan Cao, Fang Fang, Ping Guo, Zheng Lin, Wei Ma, Yi Liu


Abstract
Transforming the large amounts of unstructured text on the Internet into structured event knowledge is a critical, yet unsolved goal of NLP, especially when addressing document-level text. Existing methods struggle in Document-level Event Extraction (DEE) due to its two intrinsic challenges: (a) Nested arguments, which means one argument is the sub-string of another one. (b) Multiple events, which indicates we should identify multiple events and assemble the arguments for them. In this paper, we propose a role-interactive multi-event head attention network (CLIO) to solve these two challenges jointly. The key idea is to map different events to multiple subspaces (i.e. multi-event head). In each event subspace, we draw the semantic representation of each role closer to its corresponding arguments, then we determine whether the current event exists. To further optimize event representation, we propose an event representation enhancing strategy to regularize pre-trained embedding space to be more isotropic. Our experiments on two widely used DEE datasets show that CLIO achieves consistent improvements over previous methods.
Anthology ID:
2022.coling-1.221
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2504–2514
Language:
URL:
https://aclanthology.org/2022.coling-1.221
DOI:
Bibkey:
Cite (ACL):
Yubing Ren, Yanan Cao, Fang Fang, Ping Guo, Zheng Lin, Wei Ma, and Yi Liu. 2022. CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2504–2514, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction (Ren et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.221.pdf
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