Event Extraction by Answering (Almost) Natural Questions

Xinya Du, Claire Cardie


Abstract
The problem of event extraction requires detecting the event trigger and extracting its corresponding arguments. Existing work in event argument extraction typically relies heavily on entity recognition as a preprocessing/concurrent step, causing the well-known problem of error propagation. To avoid this issue, we introduce a new paradigm for event extraction by formulating it as a question answering (QA) task that extracts the event arguments in an end-to-end manner. Empirical results demonstrate that our framework outperforms prior methods substantially; in addition, it is capable of extracting event arguments for roles not seen at training time (i.e., in a zero-shot learning setting).
Anthology ID:
2020.emnlp-main.49
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
671–683
Language:
URL:
https://aclanthology.org/2020.emnlp-main.49
DOI:
10.18653/v1/2020.emnlp-main.49
Bibkey:
Cite (ACL):
Xinya Du and Claire Cardie. 2020. Event Extraction by Answering (Almost) Natural Questions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 671–683, Online. Association for Computational Linguistics.
Cite (Informal):
Event Extraction by Answering (Almost) Natural Questions (Du & Cardie, EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.49.pdf
Video:
 https://slideslive.com/38938649
Code
 xinyadu/eeqa +  additional community code
Data
Natural Questions