LAUSR

Emotion recognition in conversation (ERC) is important for enhancing user experience in human-computer interaction. Unlike vanilla emotion recognition in individual utterances, ERC aims to classify constituent utterances in a dialog into corresponding emotion labels, which makes contextual information crucial. In addition to contextual information, personality traits also affect emotional perception based on psychological findings. Although researchers have proposed several approaches and achieved promising results on ERC, current works in this domain rarely incorporate contextual information and personality influence. To this end, we propose a novel framework to integrate these factors seamlessly, called "Personality-enhanced Iterative Refinement Network (PIRNet)." Specifically, PIRNet is a multistage iterative method. To capture personality influence, PIRNet leverages personality traits to mimic emotional transitions and generates personality-enhanced results. Then we exploit sequence models to capture contextual information in conversations. To verify the effectiveness of our proposed method, we conduct experiments on three benchmark datasets for ERC, that is, IEMOCAP, CMU-MOSI, and CMU-MOSEI. Experimental results demonstrate that our PIRNet succeeds over currently advanced approaches to emotion recognition.