TY - GEN
T1 - Enhancing Event Causality Identification with Counterfactual Reasoning
AU - Mu, Feiteng
AU - Li, Wenjie
N1 - Publisher Copyright:
© 2023 Association for Computational Linguistics.
PY - 2023/7/9
Y1 - 2023/7/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85172197721&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
AN - SCOPUS:85172197721
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 967
EP - 975
BT - Short Papers
PB - Association for Computational Linguistics (ACL)
T2 - 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023
Y2 - 9 July 2023 through 14 July 2023
ER -