论文标题
对话中基于分布的情感识别
Distribution-based Emotion Recognition in Conversation
论文作者
论文摘要
对话中的自动情绪识别（ERC）对于情绪感知的对话人工智能至关重要。本文提出了一个基于分布的框架，该框架将ERC作为情绪分布估计的顺序到序列问题。情感的固有歧义和人类感知的主观性导致情感标签的分歧，从情绪分布的不确定性估计的角度来看，这在我们的框架中自然而然地处理。引入了贝叶斯训练损失，以通过将每个情绪状态调节在特定于话语的迪里奇先前分布上来改善不确定性估计。 IEMOCAP数据集的实验结果表明，ERC的表现优于基于单一的系统，而拟议的基于分布的ERC方法不仅具有更好的分类精度，而且还显示出改善的不确定性估计。
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.
