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.

中文翻译：

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开放域对话代理的配置文件一致性标识

保持一致的属性配置文件对于对话代理与人类自然对话至关重要。现有关于提高属性一致性的研究主要探索如何将属性信息纳入响应中，但很少努力识别响应与属性配置文件之间的一致性关系。为了促进配置文件一致性识别的研究，我们创建了一个大规模人工注释数据集，其中包含超过 110K 的单轮对话及其键值属性配置文件。响应和配置文件之间的显式关系是手动标记的。我们还提出了一个键值结构信息丰富的 BERT 模型来识别配置文件的一致性，并且它在强基线上获得了改进。