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.

提出了一个分布式框架，将自动情感识别作为序列到序列问题来解决，通过引入贝叶斯训练损失来改善情感分布的不确定性估计，从而更好地处理情感标签的不确定性，实验结果表明，分布式框架在情感分类和不确定性估计方面优于单次话语和传统的基于人工设计特征的方法。