DECN: Dialogical emotion correction network for conversational emotion recognition.

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. [ABSTRACT FROM AUTHOR]