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

TL;DR

CookDial is a new dataset of 260 human-to-human task-oriented dialogs grounded in recipes, designed to advance research in procedural knowledge understanding and complex decision-making in dialog systems.

Contribution

The paper introduces CookDial, a novel dataset with rich annotations, highlighting procedural alignment and complex agent behaviors for domain-specific dialog research.

Findings

Neural models evaluated on three challenging dialog tasks.

Dataset publicly released for further research.

Demonstrates the importance of procedural understanding in dialogs.

Abstract

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.