Event Causality Identification via Derivative Prompt Joint Learning

Shirong Shen, Heng Zhou, Tongtong Wu, Guilin Qi


Abstract
This paper studies event causality identification, which aims at predicting the causality relation for a pair of events in a sentence. Regarding event causality identification as a supervised classification task, most existing methods suffer from the problem of insufficient annotated data. In this paper, we propose a new derivative prompt joint learning model for event causality identification, which leverages potential causal knowledge in the pre-trained language model to tackle the data scarcity problem. Specifically, rather than external data or knowledge augmentation, we derive two relevant prompt tasks from event causality identification to enhance the model’s ability to identify explicit and implicit causality. We evaluate our model on two benchmark datasets and the results show that our model has great advantages over previous methods.
Anthology ID:
2022.coling-1.200
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2288–2299
Language:
URL:
https://aclanthology.org/2022.coling-1.200
DOI:
Bibkey:
Cite (ACL):
Shirong Shen, Heng Zhou, Tongtong Wu, and Guilin Qi. 2022. Event Causality Identification via Derivative Prompt Joint Learning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2288–2299, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Event Causality Identification via Derivative Prompt Joint Learning (Shen et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.200.pdf