  Consistency Identification has obtained remarkable success on open-domain
dialogue, which can be used for preventing inconsistent response generation.
However, in contrast to the rapid development in open-domain dialogue, few
efforts have been made to the task-oriented dialogue direction. In this paper,
we argue that consistency problem is more urgent in task-oriented domain. To
facilitate the research, we introduce CI-ToD, a novel dataset for Consistency
Identification in Task-oriented Dialog system. In addition, we not only
annotate the single label to enable the model to judge whether the system
response is contradictory, but also provide more fine-grained labels (i.e.,
Dialogue History Inconsistency, User Query Inconsistency and Knowledge Base
Inconsistency) to encourage model to know what inconsistent sources lead to it.
Empirical results show that state-of-the-art methods only achieve 51.3%, which
is far behind the human performance of 93.2%, indicating that there is ample
room for improving consistency identification ability. Finally, we conduct
exhaustive experiments and qualitative analysis to comprehend key challenges
and provide guidance for future directions. All datasets and models are
publicly available at \url{https://github.com/yizhen20133868/CI-ToD}.
