Exploiting Contextual Information via Dynamic Memory Network for Event Detection

Shaobo Liu, Rui Cheng, Xiaoming Yu, Xueqi Cheng


Abstract
The task of event detection involves identifying and categorizing event triggers. Contextual information has been shown effective on the task. However, existing methods which utilize contextual information only process the context once. We argue that the context can be better exploited by processing the context multiple times, allowing the model to perform complex reasoning and to generate better context representation, thus improving the overall performance. Meanwhile, dynamic memory network (DMN) has demonstrated promising capability in capturing contextual information and has been applied successfully to various tasks. In light of the multi-hop mechanism of the DMN to model the context, we propose the trigger detection dynamic memory network (TD-DMN) to tackle the event detection problem. We performed a five-fold cross-validation on the ACE-2005 dataset and experimental results show that the multi-hop mechanism does improve the performance and the proposed model achieves best F1 score compared to the state-of-the-art methods.
Anthology ID:
D18-1127
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1030–1035
Language:
URL:
https://aclanthology.org/D18-1127
DOI:
10.18653/v1/D18-1127
Bibkey:
Cite (ACL):
Shaobo Liu, Rui Cheng, Xiaoming Yu, and Xueqi Cheng. 2018. Exploiting Contextual Information via Dynamic Memory Network for Event Detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1030–1035, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Exploiting Contextual Information via Dynamic Memory Network for Event Detection (Liu et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1127.pdf
Code
 AveryLiu/TD-DMN