  Maintaining a consistent attribute profile is crucial for dialogue agents to
naturally converse with humans. Existing studies on improving attribute
consistency mainly explored how to incorporate attribute information in the
responses, but few efforts have been made to identify the consistency relations
between response and attribute profile. To facilitate the study of profile
consistency identification, we create a large-scale human-annotated dataset
with over 110K single-turn conversations and their key-value attribute
profiles. Explicit relation between response and profile is manually labeled.
We also propose a key-value structure information enriched BERT model to
identify the profile consistency, and it gained improvements over strong
baselines. Further evaluations on downstream tasks demonstrate that the profile
consistency identification model is conducive for improving dialogue
consistency.
