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Previous attempts to build effective semantic parsers for Wizard-of-Oz (WOZ)conversations suffer from the difficulty in acquiring a high-quality, manuallyannotated training set. Approaches based only on dialogue synthesis areinsufficient, as dialogues generated from state-machine based models are poorapproximations of real-life conversations. Furthermore, previously proposeddialogue state representations are ambiguous and lack the precision necessaryfor building an effective agent. This paper proposes a new dialoguerepresentation and a sample-efficient methodology that can predict precisedialogue states in WOZ conversations. We extended the ThingTalk representationto capture all information an agent needs to respond properly. Our trainingstrategy is sample-efficient: we combine (1) fewshot data sparsely sampling thefull dialogue space and (2) synthesized data covering a subset space ofdialogues generated by a succinct state-based dialogue model. The completenessof the extended ThingTalk language is demonstrated with a fully operationalagent, which is also used in training data synthesis. We demonstrate theeffectiveness of our methodology on MultiWOZ 3.0, a reannotation of theMultiWOZ 2.1 dataset in ThingTalk. ThingTalk can represent 98% of the testturns, while the simulator can emulate 85% of the validation set. We train acontextual semantic parser using our strategy, and obtain 79% turn-by-turnexact match accuracy on the reannotated test set.

A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation