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…
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