This work presents a new dialog dataset, CookDial, that facilitates research
on task-oriented dialog systems with procedural knowledge understanding. The
corpus contains 260 human-to-human task-oriented dialogs in which an agent,
given a recipe document, guides the user to cook a dish. Dialogs in CookDial
exhibit two unique features: (i) procedural alignment between the dialog flow
and supporting document; (ii) complex agent decision-making that involves
segmenting long sentences, paraphrasing hard instructions and resolving
coreference in the dialog context. In addition, we identify three challenging
(sub)tasks in the assumed task-oriented dialog system: (1) User Question
Understanding, (2) Agent Action Frame Prediction, and (3) Agent Response
Generation. For each of these tasks, we develop a neural baseline model, which
we evaluate on the CookDial dataset. We publicly release the CookDial dataset,
comprising rich annotations of both dialogs and recipe documents, to stimulate
further research on domain-specific document-grounded dialog systems.

本研究提供了一个新的对话数据集 CookDial，该数据集在任务导向的对话系统中将过程知识理解与复杂代理决策相结合。为了应对任务导向的对话系统中的三个挑战性子任务，并针对每个任务开发了一个神经基准模型，并在 CookDial 数据集上进行了评估。