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- W4308758432 abstract "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." @default.
- W4308758432 created "2022-11-15" @default.
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- W4308758432 date "2022-11-09" @default.
- W4308758432 modified "2023-09-28" @default.
- W4308758432 title "Distribution-based Emotion Recognition in Conversation" @default.
- W4308758432 doi "https://doi.org/10.48550/arxiv.2211.04834" @default.
- W4308758432 hasPublicationYear "2022" @default.
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