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State-Machine-Based Dialogue Agents with Few-Shot Contextual Semantic Parsers

by   Giovanni Campagna, et al.

This paper presents a methodology and toolkit for creating a rule-based multi-domain conversational agent for transactions from (1) language annotations of the domains' database schemas and APIs and (2) a couple of hundreds of annotated human dialogues. There is no need for a large annotated training set, which is expensive to acquire. The toolkit uses a pre-defined abstract dialogue state machine to synthesize millions of dialogues based on the domains' information. The annotated and synthesized data are used to train a contextual semantic parser that interprets the user's latest utterance in the context of a formal representation of the conversation up to that point. Developers can refine the state machine to achieve higher accuracy. On the MultiWOZ benchmark, we achieve over 71 cleaned, reannotated test set, without using any of the original training data. Our state machine can model 96 improves by 9 showing the benefit of synthesizing data using the state machine.


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