Zero-shot Event Extraction via Transfer Learning: Challenges and Insights

Qing Lyu, Hongming Zhang, Elior Sulem, Dan Roth


Abstract
Event extraction has long been a challenging task, addressed mostly with supervised methods that require expensive annotation and are not extensible to new event ontologies. In this work, we explore the possibility of zero-shot event extraction by formulating it as a set of Textual Entailment (TE) and/or Question Answering (QA) queries (e.g. “A city was attacked” entails “There is an attack”), exploiting pretrained TE/QA models for direct transfer. On ACE-2005 and ERE, our system achieves acceptable results, yet there is still a large gap from supervised approaches, showing that current QA and TE technologies fail in transferring to a different domain. To investigate the reasons behind the gap, we analyze the remaining key challenges, their respective impact, and possible improvement directions.
Anthology ID:
2021.acl-short.42
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
322–332
Language:
URL:
https://aclanthology.org/2021.acl-short.42
DOI:
10.18653/v1/2021.acl-short.42
Bibkey:
Cite (ACL):
Qing Lyu, Hongming Zhang, Elior Sulem, and Dan Roth. 2021. Zero-shot Event Extraction via Transfer Learning: Challenges and Insights. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 322–332, Online. Association for Computational Linguistics.
Cite (Informal):
Zero-shot Event Extraction via Transfer Learning: Challenges and Insights (Lyu et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-short.42.pdf
Optional supplementary material:
 2021.acl-short.42.OptionalSupplementaryMaterial.zip
Video:
 https://aclanthology.org/2021.acl-short.42.mp4
Data
MultiNLIQAMR