ThingTalk: An Extensible, Executable Representation Language for Task-Oriented Dialogues
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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
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2021Constrained Language Models Yield Few-Shot Semantic Parserscited by 162
- 2020Task-Oriented Dialogue as Dataflow Synthesiscited by 122
- 2018Semantic Parsing for Task Oriented Dialog using Hierarchical Representationscited by 101
- 2020Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Trackingcited by 65
- 2020Conversational Semantic Parsing for Dialog State Trackingcited by 40
- 2020AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Datacited by 29
- 2021Semantic Representation for Dialogue Modelingcited by 24
- 2019Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commandscited by 19
- 2020A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representationcited by 8
Built on by
Citing papers per year: 2022: 3; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
6 papers.
- 2022"Do you follow me?": A Survey of Recent Approaches in Dialogue State Trackingcited by 22
- 2022Dialogue Meaning Representation for Task-Oriented Dialogue Systemscited by 3
- 2023ReactGenie: A Development Framework for Complex Multimodal Interactions Using Large Language Modelscited by 3
- 2022Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild?cited by 1
- 2024Benchmarks Underestimate the Readiness of Multi-lingual Dialogue Agentscited by 1
- 2026WIRE: Profiling Witnessed Within-Policy Instruction Collisions in LLM Agentscited by 0
- 2026WIRE: Profiling Witnessed Within-Policy Instruction Collisions in LLM Agents27 May 2026
- 2024Benchmarks Underestimate the Readiness of Multi-lingual Dialogue Agents28 May 2024
- 2023ReactGenie: A Development Framework for Complex Multimodal Interactions Using Large Language Models16 Jun 2023
- 2022Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild?20 Dec 2022
- 2022"Do you follow me?": A Survey of Recent Approaches in Dialogue State Tracking29 Jul 2022
- 2022Dialogue Meaning Representation for Task-Oriented Dialogue Systems23 Apr 2022