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

arXiv: https://arxiv.org/abs/2203.12751 · HTML: https://mlsanity.com/paper/2203.12751/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Constrained Language Models Yield Few-Shot Semantic Parsers](https://mlsanity.com/paper/2104.08768/) (2021)
- [Task-Oriented Dialogue as Dataflow Synthesis](https://mlsanity.com/paper/2009.11423/) (2020)
- [Semantic Parsing for Task Oriented Dialog using Hierarchical Representations](https://mlsanity.com/paper/1810.07942/) (2018)
- [Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2005.00891/) (2020)
- [Conversational Semantic Parsing for Dialog State Tracking](https://mlsanity.com/paper/2010.12770/) (2020)
- [AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data](https://mlsanity.com/paper/2010.04806/) (2020)
- [Semantic Representation for Dialogue Modeling](https://mlsanity.com/paper/2105.10188/) (2021)
- [Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands](https://mlsanity.com/paper/1904.09020/) (2019)
- [A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation](https://mlsanity.com/paper/2009.07968/) (2020)

## Built on by (most cited first, 6 of 6)

- ["Do you follow me?": A Survey of Recent Approaches in Dialogue State Tracking](https://mlsanity.com/paper/2207.14627/) (2022)
- [Dialogue Meaning Representation for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2204.10989/) (2022)
- [ReactGenie: A Development Framework for Complex Multimodal Interactions Using Large Language Models](https://mlsanity.com/paper/2306.09649/) (2023)
- [Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild?](https://mlsanity.com/paper/2212.10504/) (2022)
- [Benchmarks Underestimate the Readiness of Multi-lingual Dialogue Agents](https://mlsanity.com/paper/2405.17840/) (2024)
- [WIRE: Profiling Witnessed Within-Policy Instruction Collisions in LLM Agents](https://mlsanity.com/paper/2605.27784/) (2026)

## 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.