  Previous attempts to build effective semantic parsers for Wizard-of-Oz (WOZ)
conversations suffer from the difficulty in acquiring a high-quality, manually
annotated training set. Approaches based only on dialogue synthesis are
insufficient, as dialogues generated from state-machine based models are poor
approximations of real-life conversations. Furthermore, previously proposed
dialogue state representations are ambiguous and lack the precision necessary
for building an effective agent. This paper proposes a new dialogue
representation and a sample-efficient methodology that can predict precise
dialogue states in WOZ conversations. We extended the ThingTalk representation
to capture all information an agent needs to respond properly. Our training
strategy is sample-efficient: we combine (1) fewshot data sparsely sampling the
full dialogue space and (2) synthesized data covering a subset space of
dialogues generated by a succinct state-based dialogue model. The completeness
of the extended ThingTalk language is demonstrated with a fully operational
agent, which is also used in training data synthesis. We demonstrate the
effectiveness of our methodology on MultiWOZ 3.0, a reannotation of the
MultiWOZ 2.1 dataset in ThingTalk. ThingTalk can represent 98% of the test
turns, while the simulator can emulate 85% of the validation set. We train a
contextual semantic parser using our strategy, and obtain 79% turn-by-turn
exact match accuracy on the reannotated test set.
