  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 settings, such as zero-shot
on MultiWOZ. In addition, we release STARv2, an updated version of the STAR
dataset with richer annotations, for benchmarking zero-shot end-to-end TOD
models.
