  Functionality and dialogue experience are two important factors of
task-oriented dialogue systems. Conventional approaches with closed schema
(e.g., conversational semantic parsing) often fail as both the functionality
and dialogue experience are strongly constrained by the underlying schema. We
introduce a new paradigm for task-oriented dialogue - Dialog2API - to greatly
expand the functionality and provide seamless dialogue experience. The
conversational model interacts with the environment by generating and executing
programs triggering a set of pre-defined APIs. The model also manages the
dialogue policy and interact with the user through generating appropriate
natural language responses. By allowing generating free-form programs,
Dialog2API supports composite goals by combining different APIs, whereas
unrestricted program revision provides natural and robust dialogue experience.
To facilitate Dialog2API, the core model is provided with API documents, an
execution environment and optionally some example dialogues annotated with
programs. We propose an approach tailored for the Dialog2API, where the
dialogue states are represented by a stack of programs, with most recently
mentioned program on the top of the stack. Dialog2API can work with many
application scenarios such as software automation and customer service. In this
paper, we construct a dataset for AWS S3 APIs and present evaluation results of
in-context learning baselines.
