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
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2504–2514
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.221
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.coling-1.221.pdf
- Data
- WikiEvents