ChatMDG: A discourse parsing graph fusion based approach for multi-party dialogue generation

Abstract

Comprehending multi-party dialogue generation poses a challenge due to intricate speaker interactions, where multiple participants engage in a dynamic exchange of questions and responses, assuming diverse roles such as speaker, receiver, and observer, with these roles evolving across conversational turns. Most existing research on multi-party dialogue generation only considers semantic information contained in each sentence and does not take into account the dialogue flow information implicit in multi-role interaction, leading to difficulties in accurately understanding the dialogue state in multi-party dialogue. To fill these gaps, we introduce an information fusion based approach for Multi-party Dialogue Generation named ChatMDG, which integrates role interaction into a semantic-enriched graph with context-based embeddings to cooperatively capture both global and local information in multi-party dialogue. Specifically, we proposes a graph-based network to represent the complex role-interaction dialogue structure for discourse parsing and then designs the dialogue flow encoding method to fuse role-interaction information with semantic states effectively. Furthermore, ChatMDG presents interaction strategies to correspondingly generate reactive and proactive utterances based on the fused embeddings, which lead to more dialogue coherence and user engagement. Experimental results show that ChatMDG significantly improves the accuracy of the multi-party response generation task, especially in complex scenarios with multiple interactions.