Dianxin kexue (May 2024)
Emotion recognition in conversations based on discourse parsing and graph attention network
Abstract
The research on emotion recognition in conversations (ERC) focuses on the interrelationship between conversational context and speaker modeling. The current research usually ignores the dependency within the conversation, which leads to the weak connection between the context of the conversation and the lack of logic between the speakers. Therefore, an emotion recognition in conversations model based on discourse parsing and graph attention network (DPGAT) was proposed to integrate the inter-dependency of conversation into the context modeling to make contextual information more dependent and global. Firstly, the discourse dependency relationships within the conversation were obtained through discourse parsing, and the discourse dependency graph and the speaker relationship graph were constructed. Subsequently, different types of speaker diagrams were internally integrated by multi-head attention mechanisms. Based on the graph attention network, cyclic learning was combined with dependency relationships to achieve the effective integration of contextual information and speaker information, realizing the external integration of context-related information in conversations. Finally, by analyzing the results of internal and external integration, the complete conversation context was restored, and the speaker's emotions were analyzed. By evaluating and verifying on English dataset MELD, EmoryNLP, DailyDialog and Chinese dataset M3ED, F1 scores were 66.23%, 40.03%, 59.28% and 52.77%, respectively. Compared with mainstream models, the proposed model at least reaches state-of-the-art, and can be used in different language scenarios.
