Abstract
Existing methods for event causality identification (ECI) focus on mining potential causal signals, i.e., causal context keywords and event pairs. However, causal signals are ambiguous, which may lead to the context-keywords bias and the event-pairs bias. To solve this issue, we propose the counterfactual reasoning that explicitly estimates the influence of context keywords and event pairs in training, so that we are able to eliminate the biases in inference.Experiments are conducted on two datasets, the result demonstrates the effectiveness of our method.- Anthology ID:
- 2023.acl-short.83
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 967–975
- Language:
- URL:
- https://aclanthology.org/2023.acl-short.83
- DOI:
- 10.18653/v1/2023.acl-short.83
- Cite (ACL):
- Feiteng Mu and Wenjie Li. 2023. Enhancing Event Causality Identification with Counterfactual Reasoning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 967–975, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Enhancing Event Causality Identification with Counterfactual Reasoning (Mu & Li, ACL 2023)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2023.acl-short.83.pdf