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.

翻译：谈话中的自动情感识别( ERC) 对情绪觉悟的人工对话智能至关重要。 本文提出了一个基于分配的框架, 将 ERC 设计成情感分布估计的顺序到顺序问题。 情感的内在模糊性和人类感知的主观性导致情感标签的分歧, 在我们的框架中,从情感分布的不确定性估计的角度自然地处理情感标签问题。 引入了巴耶斯人的培训损失,通过调整每个情绪状态的发音方式来改善不确定性的估计。 IEMOCAP 数据集的实验结果显示, ERC 优于单层系统, 以及拟议的基于分配的 ERC 方法不仅提高了分类的准确性, 也显示了不确定性的改善。