CookDial: a dataset for task-oriented dialogs grounded in procedural documents

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.

Automated summary

This work introduces a new dialog dataset, CookDial, for research on task-oriented dialog systems with procedural knowledge understanding. The dataset consists of 260 human-to-human task-oriented dialogs in which an agent guides the user to cook a dish based on a recipe document. CookDial dialogs exhibit procedural alignment and complex agent decision-making, and three challenging tasks are identified. Neural baseline models are developed for each task and evaluated on the CookDial dataset. The dataset is publicly released to stimulate further research on domain-specific document-grounded dialog systems.