ThingTalk: An Extensible, Executable Representation Language for Task-Oriented Dialogues
This addresses the challenge of developing transactional conversational agents more efficiently and accurately, though it appears incremental as it builds on existing representation languages.
The paper tackles the problem of semantic parsing for task-oriented dialogues by proposing ThingTalk, an extensible, executable representation language, which achieves a new state-of-the-art accuracy of 79% turn-by-turn on the MultiWOZ benchmark.
Task-oriented conversational agents rely on semantic parsers to translate natural language to formal representations. In this paper, we propose the design and rationale of the ThingTalk formal representation, and how the design improves the development of transactional task-oriented agents. ThingTalk is built on four core principles: (1) representing user requests directly as executable statements, covering all the functionality of the agent, (2) representing dialogues formally and succinctly to support accurate contextual semantic parsing, (3) standardizing types and interfaces to maximize reuse between agents, and (4) allowing multiple, independently-developed agents to be composed in a single virtual assistant. ThingTalk is developed as part of the Genie Framework that allows developers to quickly build transactional agents given a database and APIs. We compare ThingTalk to existing representations: SMCalFlow, SGD, TreeDST. Compared to the others, the ThingTalk design is both more general and more cost-effective. Evaluated on the MultiWOZ benchmark, using ThingTalk and associated tools yields a new state of the art accuracy of 79% turn-by-turn.