A Unified Dialogue Framework Based on Joint Generation of Dialogue Acts and Responses

Abstract

Currently, building an end-to-end dialogue system for multi-domain task-oriented dialogue has some enormous challenges. Dialogue systems must obtain entire dialogue states from all relevant domains in order to respond correctly. However, multiple domains are involved in the actual dialogue process, which increases the difficulty of obtaining the dialogue state. In addition, dialogue systems process diverse information such as dialogue history, dialogue state, dialogue act, and database across domains, resulting in natural responses. These complex dialogue information brings greater difficulties to response generation. In this paper, we propose a novel unified dialogue framework for multi-domain dialogue in task-oriented dialog, including three modules: Encoder, Dialogue State Tracker and Multiple Decoders. First, encoder module encode all text input into continuous representations. Secondly, we train the dialogue state tracker module with a stacked-attention architecture.It learns information from slot-atten structure and domain-atten structure to track dialogue state. Then, multiple decoders module consists of act decoder structure and response decoder structure. It combines information from different textual inputs while modeling dialogue act. Finally, we jointly train all the above modules to generate system responses. We conducted extensive experiments on the dataset MultiWOZ. The experimental results show that our model achieves state-of-the-art results on evaluation metrics compared to models from previous work.