Event Detection with Dual Relational Graph Attention Networks

Jiaxin Mi, Po Hu, Peng Li


Abstract
Event detection, which aims to identify instances of specific event types from pieces of text, is a fundamental task in information extraction. Most existing approaches leverage syntactic knowledge with a set of syntactic relations to enhance event detection. However, a side effect of these syntactic-based approaches is that they may confuse different syntactic relations and tend to introduce redundant or noisy information, which may lead to performance degradation. To this end, we propose a simple yet effective model named DualGAT (Dual Relational Graph Attention Networks), which exploits the complementary nature of syntactic and semantic relations to alleviate the problem. Specifically, we first construct a dual relational graph that both aggregates syntactic and semantic relations to the key nodes in the graph, so that event-relevant information can be comprehensively captured from multiple perspectives (i.e., syntactic and semantic views). We then adopt augmented relational graph attention networks to encode the graph and optimize its attention weights by introducing contextual information, which further improves the performance of event detection. Extensive experiments conducted on the standard ACE2005 benchmark dataset indicate that our method significantly outperforms the state-of-the-art methods and verifies the superiority of DualGAT over existing syntactic-based methods.
Anthology ID:
2022.coling-1.172
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:
1979–1989
Language:
URL:
https://aclanthology.org/2022.coling-1.172
DOI:
Bibkey:
Cite (ACL):
Jiaxin Mi, Po Hu, and Peng Li. 2022. Event Detection with Dual Relational Graph Attention Networks. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1979–1989, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Event Detection with Dual Relational Graph Attention Networks (Mi et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.172.pdf
Code
 macvh/dualgat