  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.
