Multi-Domain Dialogue Acts and Response Co-Generation
This work addresses the problem of improving response generation for task-oriented dialogue systems, which is incremental as it builds on existing methods by co-generating acts and responses.
The paper tackles the problem of generating fluent and informative responses in task-oriented dialogue systems by addressing shortcomings in existing pipeline approaches, such as neglecting multi-domain dialogue act structures and semantic associations, and achieves favorable improvements over state-of-the-art models on the MultiWOZ dataset in automatic and human evaluations.
Generating fluent and informative responses is of critical importance for task-oriented dialogue systems. Existing pipeline approaches generally predict multiple dialogue acts first and use them to assist response generation. There are at least two shortcomings with such approaches. First, the inherent structures of multi-domain dialogue acts are neglected. Second, the semantic associations between acts and responses are not taken into account for response generation. To address these issues, we propose a neural co-generation model that generates dialogue acts and responses concurrently. Unlike those pipeline approaches, our act generation module preserves the semantic structures of multi-domain dialogue acts and our response generation module dynamically attends to different acts as needed. We train the two modules jointly using an uncertainty loss to adjust their task weights adaptively. Extensive experiments are conducted on the large-scale MultiWOZ dataset and the results show that our model achieves very favorable improvement over several state-of-the-art models in both automatic and human evaluations.