JOINT LEARNING FOR ADDRESSEE SELECTION AND RESPONSE GENERATION IN MULTI-PARTY CONVERSATION

A large number of multi-party conversation scenarios exist in social networks, which have been seldom studied in the field of human-machine conversation. In this paper, we study a novel task of joint learning for addressee selection and response generation in multi-party conversations. Systems are expected to select whom they address and generate the corresponding response. To solve it, we propose an end-to-end addressee selection and response generation (ASRG) model, containing an addressee selection module and a response generation module. In the selection module, we develop an addressee prediction attention scheme to obtain a unique context vector for each candidate, thereby calculating the probability of the candidate more accurately. In the generation module, we propose a Focus Transformer to generate responses. These two modules are jointly learnt to fully explore the correlations between addressee and response. Experimental results show ASRG remarkably outperforms baselines and generates relevant content for different addressees.