Abstract
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.
Highlights
Task-oriented dialogue systems aim to facilitate people with such services as hotel reservation and ticket booking through natural language conversations
A standard architecture of these systems generally decomposes this task into several subtasks, including natural language understanding (Gupta et al, 2018), dialogue state tracking (Zhong et al, 2018) and natural language
We propose a co-generation model to generate act and response sequences jointly, with an uncertainty loss used for adaptive weighting
Summary
Task-oriented dialogue systems aim to facilitate people with such services as hotel reservation and ticket booking through natural language conversations. Recent years have seen a rapid proliferation of interests in this task from both academia and industry (Bordes et al, 2017; Budzianowski et al, 2018; Wu et al, 2019). A standard architecture of these systems generally decomposes this task into several subtasks, including natural language understanding (Gupta et al, 2018), dialogue state tracking (Zhong et al, 2018) and natural language Dialogue Example User. I'm looking for an expensive Indian restaurant. It serves Indian food and is in the expensive price range. Can I get their address and phone number? How about Curry Garden? It serves Indian food and is in the expensive price range.
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