mlsanityData to 7 Oct 2026

ThingTalk: An Extensible, Executable Representation Language for Task-Oriented Dialogues

arXiv 2203.12751 ↗First posted 23 Mar 2022cs.PL cs.CLcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 10 of its 22 references that are in the map. First posted on arXiv 23 Mar 2022.

Builds on

10 of 22 references.

References · 10

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  2. 2021Constrained Language Models Yield Few-Shot Semantic Parserscited by 162
  3. 2020Task-Oriented Dialogue as Dataflow Synthesiscited by 122
  4. 2018Semantic Parsing for Task Oriented Dialog using Hierarchical Representationscited by 101
  5. 2020Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Trackingcited by 65
  6. 2020Conversational Semantic Parsing for Dialog State Trackingcited by 40
  7. 2020AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Datacited by 29
  8. 2021Semantic Representation for Dialogue Modelingcited by 24
  9. 2019Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commandscited by 19
  10. 2020A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representationcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 1; 2025: 0; 2026: 1.

6 papers.