Event Extraction as Multi-turn Question Answering

Fayuan Li, Weihua Peng, Yuguang Chen, Quan Wang, Lu Pan, Yajuan Lyu, Yong Zhu


Abstract
Event extraction, which aims to identify event triggers of pre-defined event types and their arguments of specific roles, is a challenging task in NLP. Most traditional approaches formulate this task as classification problems, with event types or argument roles taken as golden labels. Such approaches fail to model rich interactions among event types and arguments of different roles, and cannot generalize to new types or roles. This work proposes a new paradigm that formulates event extraction as multi-turn question answering. Our approach, MQAEE, casts the extraction task into a series of reading comprehension problems, by which it extracts triggers and arguments successively from a given sentence. A history answer embedding strategy is further adopted to model question answering history in the multi-turn process. By this new formulation, MQAEE makes full use of dependency among arguments and event types, and generalizes well to new types with new argument roles. Empirical results on ACE 2005 shows that MQAEE outperforms current state-of-the-art, pushing the final F1 of argument extraction to 53.4% (+2.0%). And it also has a good generalization ability, achieving competitive performance on 13 new event types even if trained only with a few samples of them.
Anthology ID:
2020.findings-emnlp.73
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
829–838
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.73
DOI:
10.18653/v1/2020.findings-emnlp.73
Bibkey:
Cite (ACL):
Fayuan Li, Weihua Peng, Yuguang Chen, Quan Wang, Lu Pan, Yajuan Lyu, and Yong Zhu. 2020. Event Extraction as Multi-turn Question Answering. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 829–838, Online. Association for Computational Linguistics.
Cite (Informal):
Event Extraction as Multi-turn Question Answering (Li et al., Findings 2020)
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PDF:
https://aclanthology.org/2020.findings-emnlp.73.pdf