Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction

Senhui Zhang, Tao Ji, Wendi Ji, Xiaoling Wang


Abstract
Event detection is a classic natural language processing task. However, the constantly emerging new events make supervised methods not applicable to unseen types. Previous zero-shot event detection methods either require predefined event types as heuristic rules or resort to external semantic analyzing tools. To overcome this weakness, we propose an end-to-end framework named Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction (ZEOP). By creatively introducing multiple contrastive samples with ordered similarities, the encoder can learn event representations from both instance-level and class-level, which makes the distinctions between different unseen types more significant. Meanwhile, we utilize the prompt-based prediction to identify trigger words without relying on external resources. Experiments demonstrate that our model detects events more effectively and accurately than state-of-the-art methods.
Anthology ID:
2022.findings-naacl.196
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2572–2580
Language:
URL:
https://aclanthology.org/2022.findings-naacl.196
DOI:
10.18653/v1/2022.findings-naacl.196
Bibkey:
Cite (ACL):
Senhui Zhang, Tao Ji, Wendi Ji, and Xiaoling Wang. 2022. Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2572–2580, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction (Zhang et al., Findings 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2022.findings-naacl.196.pdf
Video:
 https://preview.aclanthology.org/nschneid-patch-4/2022.findings-naacl.196.mp4
Code
 kindroach/naacl-zeop