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        <title>Topic-Aware Causal Intervention for Counterfactual Detection</title>
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    <abstract>Counterfactual statements, which describe events that did not or cannot take place, are beneficial to numerous NLP applications. Hence, we consider the problem of counterfactual detection (CFD) and seek to enhance the CFD models. Previous models are reliant on clue phrases to predict counterfactuality, so they suffer from significant performance drop when clue phrase hints do not exist during testing. Moreover, these models tend to predict non-counterfactuals over counterfactuals. To address these issues, we propose to integrate neural topic model into the CFD model to capture the global semantics of the input statement. We continue to causally intervene the hidden representations of the CFD model to balance the effect of the class labels. Extensive experiments show that our approach outperforms previous state-of-the-art CFD and bias-resolving methods in both the CFD and other bias-sensitive tasks.</abstract>
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%0 Conference Proceedings
%T Topic-Aware Causal Intervention for Counterfactual Detection
%A Nguyen, Thong Thanh
%A Nguyen, Truc-My
%Y Hämäläinen, Mika
%Y Öhman, Emily
%Y Miyagawa, So
%Y Alnajjar, Khalid
%Y Bizzoni, Yuri
%S Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, USA
%F nguyen-nguyen-2024-topic
%X Counterfactual statements, which describe events that did not or cannot take place, are beneficial to numerous NLP applications. Hence, we consider the problem of counterfactual detection (CFD) and seek to enhance the CFD models. Previous models are reliant on clue phrases to predict counterfactuality, so they suffer from significant performance drop when clue phrase hints do not exist during testing. Moreover, these models tend to predict non-counterfactuals over counterfactuals. To address these issues, we propose to integrate neural topic model into the CFD model to capture the global semantics of the input statement. We continue to causally intervene the hidden representations of the CFD model to balance the effect of the class labels. Extensive experiments show that our approach outperforms previous state-of-the-art CFD and bias-resolving methods in both the CFD and other bias-sensitive tasks.
%U https://aclanthology.org/2024.nlp4dh-1.16
%P 165-176

Markdown (Informal)

[Topic-Aware Causal Intervention for Counterfactual Detection](https://aclanthology.org/2024.nlp4dh-1.16) (Nguyen & Nguyen, NLP4DH 2024)

• Topic-Aware Causal Intervention for Counterfactual Detection (Nguyen & Nguyen, NLP4DH 2024)

ACL

• Thong Thanh Nguyen and Truc-My Nguyen. 2024. Topic-Aware Causal Intervention for Counterfactual Detection. In Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities, pages 165–176, Miami, USA. Association for Computational Linguistics.