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% turn-by-turn slot accuracy on a cleaned, reannotated test set, without using any of the original training data. Our state machine can model 96% of the human agent turns. Our training strategy improves by 9% over a baseline that uses the same amount of hand-labeled data, showing the benefit of synthesizing data using the state machine.

本文提出了一种新的对话表示和高效的方法，可以在Wizard-of-Oz (WOZ)对话中预测精确的对话状态，使用ThingTalk表示法表示了所有代理需要回应的信息，并在MultiWOZ 3.0上验证了该方法，获得了79%的精确匹配准确度。

一种Few-Shot语义解析器，用于具有精确ThingTalk表示的Wizard-of-Oz对话