An Emotion Evolution Network for Emotion Recognition in Conversation

Abstract

Emotion recognition in conversation (ERC) aims to detect the emotion in a conversation, which has drawn increasing interests due to its widely applications. Current methodologies mainly endeavor to capture a good representation of conversation context. However, we argue that the conversation context are not always consistent with the emotion evolution. This incongruity can greatly restrict the recognition performance. To address aforementioned challenges, in this paper, we propose an emotion evolution network for emotion recognition in conversation (E2Net). Specifically, a speaker-aware modeling methodology is firstly constructed to fuse the utterance from conversations. We employ the gated recurrent unit (GRU) encodes the utterance sequentially. For encoding the interaction between speakers, a listener state is introduced to aid in analyzing conversation context. Then, a Transformer-based method is proposed to capture the emotion evolution accompanying with the emotion transformation matrix. To demonstrate the superior performance of our proposed method, extensive experiments are conducted on four REC datasets and the experimental results suggest that our method is effective and outperforms the current state-of-the-art methods on multiple datasets.