Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms

Yubo Chen, Hang Yang, Kang Liu, Jun Zhao, Yantao Jia


Abstract
Traditional approaches to the task of ACE event detection primarily regard multiple events in one sentence as independent ones and recognize them separately by using sentence-level information. However, events in one sentence are usually interdependent and sentence-level information is often insufficient to resolve ambiguities for some types of events. This paper proposes a novel framework dubbed as Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms (HBTNGMA) to solve the two problems simultaneously. Firstly, we propose a hierachical and bias tagging networks to detect multiple events in one sentence collectively. Then, we devise a gated multi-level attention to automatically extract and dynamically fuse the sentence-level and document-level information. The experimental results on the widely used ACE 2005 dataset show that our approach significantly outperforms other state-of-the-art methods.
Anthology ID:
D18-1158
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1267–1276
Language:
URL:
https://aclanthology.org/D18-1158
DOI:
10.18653/v1/D18-1158
Bibkey:
Cite (ACL):
Yubo Chen, Hang Yang, Kang Liu, Jun Zhao, and Yantao Jia. 2018. Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1267–1276, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms (Chen et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1158.pdf
Video:
 https://aclanthology.org/D18-1158.mp4
Code
 yubochen/NBTNGMA4ED