Natural language dialogue systems raise great attention recently. As many
dialogue models are data-driven, high-quality datasets are essential to these
systems. In this paper, we introduce Pchatbot, a large-scale dialogue dataset
that contains two subsets collected from Weibo and Judicial forums
respectively. To adapt the raw dataset to dialogue systems, we elaborately
normalize the raw dataset via processes such as anonymization, deduplication,
segmentation, and filtering. The scale of Pchatbot is significantly larger than
existing Chinese datasets, which might benefit the data-driven models. Besides,
current dialogue datasets for personalized chatbot usually contain several
persona sentences or attributes. Different from existing datasets, Pchatbot
provides anonymized user IDs and timestamps for both posts and responses. This
enables the development of personalized dialogue models that directly learn
implicit user personality from the user's dialogue history. Our preliminary
experimental study benchmarks several state-of-the-art dialogue models to
provide a comparison for future work. The dataset can be publicly accessed at
Github.

介绍了 Pchatbot 数据集，该数据集是从微博和司法论坛等地收集的大规模对话数据集，通过去重、标准化、过滤等过程来适应对话系统，提供匿名用户 ID 和时间戳等隐私保护技术，为未来的个性化对话模型提供基础，公开在 Github 上。