Document-Level Event Argument Extraction by Conditional Generation

Sha Li, Heng Ji, Jiawei Han


Abstract
Event extraction has long been treated as a sentence-level task in the IE community. We argue that this setting does not match human informative seeking behavior and leads to incomplete and uninformative extraction results. We propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. We also compile a new document-level event extraction benchmark dataset WikiEvents which includes complete event and coreference annotation. On the task of argument extraction, we achieve an absolute gain of 7.6% F1 and 5.7% F1 over the next best model on the RAMS and WikiEvents dataset respectively. On the more challenging task of informative argument extraction, which requires implicit coreference reasoning, we achieve a 9.3% F1 gain over the best baseline. To demonstrate the portability of our model, we also create the first end-to-end zero-shot event extraction framework and achieve 97% of fully supervised model’s trigger extraction performance and 82% of the argument extraction performance given only access to 10 out of the 33 types on ACE.
Anthology ID:
2021.naacl-main.69
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
894–908
Language:
URL:
https://aclanthology.org/2021.naacl-main.69
DOI:
10.18653/v1/2021.naacl-main.69
Bibkey:
Cite (ACL):
Sha Li, Heng Ji, and Jiawei Han. 2021. Document-Level Event Argument Extraction by Conditional Generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 894–908, Online. Association for Computational Linguistics.
Cite (Informal):
Document-Level Event Argument Extraction by Conditional Generation (Li et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.69.pdf
Optional supplementary data:
 2021.naacl-main.69.OptionalSupplementaryData.zip
Code
 raspberryice/gen-arg
Data
WikiEvents