Emotion recognition in conversation (ERC) is an important research topic in artificial intelligence. Different from the emotion estimation in individual utterances, ERC requires proper handling of human interactions in conversations. Several approaches have been proposed for ERC and achieved promising results. In this paper, we propose a correction model for previous approaches, called “Dialogical Emotion Correction Network (DECN)”. This model aims to automatically correct some errors made by emotion recognition strategies and further improve the recognition performance. Specifically, DECN employs a graphical network to model human interactions in conversations. To further utilize the contextual information, DECN also employs the multi-head attention based bi-directional GRU component. Since DECN is a correction model for ERC, it can be easily integrated with any emotion recognition strategy. Experimental results on the IEMOCAP and MELD datasets verify the effectiveness of our proposed method. DECN can improve the performance of emotion recognition strategies with few parameters and low computational complexity.

对话中的情感识别（ERC）是人工智能中的重要研究课题。与个人话语中的情绪估计不同，ERC需要正确处理对话中的人类互动。已为ERC提出了几种方法，并取得了可喜的结果。在本文中，我们为先前的方法提出了一种校正模型，称为“对话情感校正网络（DECN）”。该模型旨在自动纠正情绪识别策略所犯的一些错误，并进一步提高识别性能。具体来说，DECN使用图形网络来模拟对话中的人类交互。为了进一步利用上下文信息，DECN还采用了基于多头注意力的双向GRU组件。由于DECN是ERC的修正模型，它可以轻松地与任何情绪识别策略集成。在IEMOCAP和MELD数据集上的实验结果证明了我们提出的方法的有效性。DECN可以以较少的参数和较低的计算复杂度来改善情绪识别策略的性能。