MIDS: End-to-End Personalized Response Generation in Untrimmed Multi-Role Dialogue

Abstract

Multi-role dialogue is a challenging issue in nature language process (NLP), which needs not only to understand the sentences, but also to simulate the interaction among roles. However, existing methods treat all roles’ speeches as one sequence and assume that only two speakers take turn to talk, which blurs the characteristics of roles and rarely happen in daily life. To address these issues, we propose a Multi-role Interposition Dialogue System (MIDS) which generates reasonable responses based on dialogue context and next speaker prediction. MIDS employs multiple role-defined encoders to understand each speaker, and an independent sequence model to predict the next speaker. The independent sequence model also works as a scheduler to integrate encoders with weights. Then, an attention-enhanced decoder generates responses based on dialogue context, speaker prediction and integrated encoders. Moreover, with the help of the unique speaker prediction, MIDS is able to generate diverse responses and join conversation actively when appropriate. Experimental results demonstrate that MIDS significantly improves the accuracy of speaker prediction and reduces the perplexity of generation over baselines. Furthermore, MIDS is able to interact with users without cue during real-life online conversations. This work marks a first step towards simulating multi-role dialogue generation.