Task2Dial: A Novel Task and Dataset for Commonsense-enhanced Task-based Dialogue Grounded in Documents

Carl Strathearn, Dimitra Gkatzia


Abstract
This paper proposes a novel task on commonsense-enhanced task-based dialogue grounded in documents and describes the Task2Dial dataset, a novel dataset of document-grounded task-based dialogues, where an Information Giver (IG) provides instructions (by consulting a document) to an Information Follower (IF), so that the latter can successfully complete the task. In this unique setting, the IF can ask clarification questions which may not be grounded in the underlying document and require commonsense knowledge to be answered. The Task2Dial dataset poses new challenges: (1) its human reference texts show more lexical richness and variation than other document-grounded dialogue datasets; (2) generating from this set requires paraphrasing as instructional responses might have been modified from the underlying document; (3) requires commonsense knowledge, since questions might not necessarily be grounded in the document; (4) generating requires planning based on context, as task steps need to be provided in order. The Task2Dial dataset contains dialogues with an average 18.15 number of turns and 19.79 tokens per turn, as compared to 12.94 and 12 respectively in existing datasets. As such, learning from this dataset promises more natural, varied and less template-like system utterances.
Anthology ID:
2022.dialdoc-1.21
Volume:
Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Song Feng, Hui Wan, Caixia Yuan, Han Yu
Venue:
dialdoc
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
187–196
Language:
URL:
https://aclanthology.org/2022.dialdoc-1.21
DOI:
10.18653/v1/2022.dialdoc-1.21
Bibkey:
Cite (ACL):
Carl Strathearn and Dimitra Gkatzia. 2022. Task2Dial: A Novel Task and Dataset for Commonsense-enhanced Task-based Dialogue Grounded in Documents. In Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering, pages 187–196, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Task2Dial: A Novel Task and Dataset for Commonsense-enhanced Task-based Dialogue Grounded in Documents (Strathearn & Gkatzia, dialdoc 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.dialdoc-1.21.pdf
Data
CoQADoc2Dialdoc2dial