基于分布的对话情感识别
Distribution-based Emotion Recognition in Conversation
AI总结：
本文提出基于分布的对话情感识别框架，将ERC建模为序列到序列的情感分布估计问题，并引入贝叶斯训练损失改进不确定性估计，在IEMOCAP上取得更好的分类准确率与不确定性估计。
摘要：
对话中的自动情感识别（ERC）对于情感感知的对话式人工智能至关重要。本文提出了一种基于分布的框架，将ERC形式化为一个用于情感分布估计的序列到序列问题。情感固有的模糊性和人类感知的主观性导致情感标签存在分歧，我们的框架从情感分布中不确定性估计的角度自然地处理了这一问题。引入了一种贝叶斯训练损失，通过将每个情感状态条件化于一个特定于话语的Dirichlet先验分布来改进不确定性估计。在IEMOCAP数据集上的实验结果表明，ERC优于基于单话语的系统，并且所提出的基于分布的ERC方法不仅具有更好的分类准确率，还显示出改进的不确定性估计。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
