AnyTOD: A Programmable Task-Oriented Dialog System
Jeffrey Zhao, Yuan Cao, Raghav Gupta, Harrison Lee, Abhinav Rastogi,, Mingqiu Wang, Hagen Soltau, Izhak Shafran, Yonghui Wu

TL;DR
AnyTOD introduces a zero-shot, programmable task-oriented dialog system that leverages a neuro-symbolic approach to adapt to unseen tasks without task-specific training, significantly reducing data needs.
Contribution
It presents a novel neuro-symbolic framework for TOD that generalizes to unseen schemas and tasks, enabling zero-shot transfer and reducing training requirements.
Findings
Achieves state-of-the-art results on STAR, ABCD, and SGD benchmarks.
Demonstrates strong zero-shot transfer in low-resource settings like MultiWOZ.
Provides STARv2 dataset with richer annotations for benchmarking.
Abstract
We propose AnyTOD, an end-to-end, zero-shot task-oriented dialog (TOD) system capable of handling unseen tasks without task-specific training. We view TOD as a program executed by a language model (LM), where program logic and ontology is provided by a designer as a schema. To enable generalization to unseen schemas and programs without prior training, AnyTOD adopts a neuro-symbolic approach. A neural LM keeps track of events occurring during a conversation and a symbolic program implementing the dialog policy is executed to recommend next actions AnyTOD should take. This approach drastically reduces data annotation and model training requirements, addressing the enduring challenge of rapidly adapting a TOD system to unseen tasks and domains. We demonstrate state-of-the-art results on STAR, ABCD and SGD benchmarks. We also demonstrate strong zero-shot transfer ability in low-resource…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multimodal Machine Learning Applications
MethodsStochastic Gradient Descent · Ontology
