Modelling Personalized Dialogue Generation in Multi-Party Settings

Rohan Kumar, Dushyant Singh Chauhan, Gaël Dias, Asif Ekbal

Abstract: Naive dialogue generation systems do not have the ability to generate distinguishable utterances while replying to different speakers, as they do not take into consideration whom the speaker is. To overcome this situation, we present an end-to-end deep learning model that relies on a person-specific embedding (persona) to generate adequate responses in multi-party conversations. In particular, the persona contains information about how a person behaves in the conversational multi-party setting. Empirical results on the Multi-Domain Wizard-of-Oz (MultiWoz) data set show the efficacy of our approach over the existing state-of-the-art systems, and show that our approach efficiently generates person-specific utterances.