Global Constraints with Prompting for Zero-Shot Event Argument Classification

Zizheng Lin, Hongming Zhang, Yangqiu Song


Abstract
Determining the role of event arguments is a crucial subtask of event extraction. Most previous supervised models leverage costly annotations, which is not practical for open-domain applications. In this work, we propose to use global constraints with prompting to effectively tackles event argument classification without any annotation and task-specific training. Specifically, given an event and its associated passage, the model first creates several new passages by prefix prompts and cloze prompts, where prefix prompts indicate event type and trigger span, and cloze prompts connect each candidate role with the target argument span. Then, a pre-trained language model scores the new passages, making the initial prediction. Our novel prompt templates can easily adapt to all events and argument types without manual effort. Next, the model regularizes the prediction by global constraints exploiting cross-task, cross-argument, and cross-event relations. Extensive experiments demonstrate our model’s effectiveness: it outperforms the best zero-shot baselines by 12.5% and 10.9% F1 on ACE and ERE with given argument spans and by 4.3% and 3.3% F1, respectively, without given argument spans. We have made our code publicly available.
Anthology ID:
2023.findings-eacl.191
Volume:
Findings of the Association for Computational Linguistics: EACL 2023
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2527–2538
Language:
URL:
https://aclanthology.org/2023.findings-eacl.191
DOI:
10.18653/v1/2023.findings-eacl.191
Bibkey:
Cite (ACL):
Zizheng Lin, Hongming Zhang, and Yangqiu Song. 2023. Global Constraints with Prompting for Zero-Shot Event Argument Classification. In Findings of the Association for Computational Linguistics: EACL 2023, pages 2527–2538, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Global Constraints with Prompting for Zero-Shot Event Argument Classification (Lin et al., Findings 2023)
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PDF:
https://aclanthology.org/2023.findings-eacl.191.pdf
Video:
 https://aclanthology.org/2023.findings-eacl.191.mp4