ACL 2023short13 citations
Enhancing Event Causality Identification with Counterfactual Reasoning
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.
BibTeX
@inproceedings{mu-li-2023-enhancing,
title = "Enhancing Event Causality Identification with Counterfactual Reasoning",
author = "Mu, Feiteng and
Li, Wenjie",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.83/",
doi = "10.18653/v1/2023.acl-short.83",
pages = "967--975"
}