A Training-Free Debiasing Framework with Counterfactual Reasoning for Conversational Emotion Detection
Geng Tu, Ran Jing, Bin Liang, Min Yang, Kam-Fai Wong, Ruifeng Xu
Abstract
Unintended dataset biases typically exist in existing Emotion Recognition in Conversations (ERC) datasets, including label bias, where models favor the majority class due to imbalanced training data, as well as the speaker and neutral word bias, where models make unfair predictions because of excessive correlations between specific neutral words or speakers and classes. However, previous studies in ERC generally focus on capturing context-sensitive and speaker-sensitive dependencies, ignoring the unintended dataset biases of data, which hampers the generalization and fairness in ERC. To address this issue, we propose a Training-Free Debiasing framework (TFD) that operates during prediction without additional training. To ensure compatibility with various ERC models, it does not balance data or modify the model structure. Instead, TFD extracts biases from the model by generating counterfactual utterances and contexts and mitigates them using simple yet empirically robust element-wise subtraction operations. Extensive experiments on three public datasets demonstrate that TFD effectively improves generalization ability and fairness across different ERC models.- Anthology ID:
- 2023.emnlp-main.967
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 15639–15650
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.967
- DOI:
- 10.18653/v1/2023.emnlp-main.967
- Cite (ACL):
- Geng Tu, Ran Jing, Bin Liang, Min Yang, Kam-Fai Wong, and Ruifeng Xu. 2023. A Training-Free Debiasing Framework with Counterfactual Reasoning for Conversational Emotion Detection. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15639–15650, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- A Training-Free Debiasing Framework with Counterfactual Reasoning for Conversational Emotion Detection (Tu et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.emnlp-main.967.pdf