  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.
