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.

本研究提出了一种基于任务型对话的新模式-Dialog2API，通过生成和执行程序来触发一组预定义API，该模型还管理对话策略并通过生成适当的自然语言响应与用户交互，使功能和对话体验大大扩展，通过允许生成自由格式的程序，Dialog2API支持组合目标，这篇论文构建了一个AWS S3 APIs数据集，并提出了一种适用于Dialog2API的方法，其中对话状态由程序堆栈表示，最近提到的程序在堆栈的顶部。

Dialog2API：基于API描述和示例程序的面向任务的对话