Causal Intervention Improves Implicit Sentiment Analysis

Siyin Wang, Jie Zhou, Changzhi Sun, Junjie Ye, Tao Gui, Qi Zhang, Xuanjing Huang


Abstract
Despite having achieved great success for sentiment analysis, existing neural models struggle with implicit sentiment analysis. It is because they may latch onto spurious correlations (“shortcuts”, e.g., focusing only on explicit sentiment words), resulting in undermining the effectiveness and robustness of the learned model. In this work, we propose a CausaL intervention model for implicit sEntiment ANalysis using instrumental variable (CLEAN). We first review sentiment analysis from a causal perspective and analyze the confounders existing in this task. Then, we introduce instrumental variable to eliminate the confounding causal effects, thus extracting the pure causal effect between sentence and sentiment. We compare the proposed CLEAN with several strong baselines on both the general implicit sentiment analysis and aspect-based implicit sentiment analysis tasks. The results indicate the great advantages of our model and the efficacy of implicit sentiment reasoning.
Anthology ID:
2022.coling-1.607
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:
6966–6977
Language:
URL:
https://aclanthology.org/2022.coling-1.607
DOI:
Bibkey:
Cite (ACL):
Siyin Wang, Jie Zhou, Changzhi Sun, Junjie Ye, Tao Gui, Qi Zhang, and Xuanjing Huang. 2022. Causal Intervention Improves Implicit Sentiment Analysis. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6966–6977, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Causal Intervention Improves Implicit Sentiment Analysis (Wang et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.607.pdf