Distribution-based Emotion Recognition in Conversation
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Abstract (arXiv)
Automatic emotion recognition in conversation (ERC) is crucial for emotion-aware conversational artificial intelligence. This paper proposes a distribution-based framework that formulates ERC as a sequence-to-sequence problem for emotion distribution estimation. The inherent ambiguity of emotions and the subjectivity of human perception lead to disagreements in emotion labels, which is handled naturally in our framework from the perspective of uncertainty estimation in emotion distributions. A Bayesian training loss is introduced to improve the uncertainty estimation by conditioning each emotional state on an utterance-specific Dirichlet prior distribution. Experimental results on the IEMOCAP dataset show that ERC outperformed the single-utterance-based system, and the proposed distribution-based ERC methods have not only better classification accuracy, but also show improved uncertainty estimation.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 31 of its 55 references that are in the map. First posted on arXiv 9 Nov 2022.
Builds on
31 of 55 references.
References · 31
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19 more
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2 papers.