Passage retrieval is a fundamental task in information retrieval (IR) research, which has drawn much attention recently. In the English field, the availability of large-scale annotated dataset (e.g, MS MARCO) and the emergence of deep pre-trained language models (e.g, BERT) has resulted in a substantial improvement of existing passage retrieval systems. However, in the Chinese field, especially for specific domains, passage retrieval systems are still immature due to quality-annotated dataset being limited by scale. Therefore, in this paper, we present a novel multi-domain Chinese dataset for passage retrieval (Multi-CPR). The dataset is collected from three different domains, including E-commerce, Entertainment video and Medical. Each dataset contains millions of passages and a certain amount of human annotated query-passage related pairs. We implement various representative passage retrieval methods as baselines. We find that the performance of retrieval models trained on dataset from general domain will inevitably decrease on specific domain. Nevertheless, a passage retrieval system built on in-domain annotated dataset can achieve significant improvement, which indeed demonstrates the necessity of domain labeled data for further optimization. We hope the release of the Multi-CPR dataset could benchmark Chinese passage retrieval task in specific domain and also make advances for future studies.

翻译：在英文领域,大规模附加说明的数据集(例如MS MARCO)的提供和经过深层次预先培训的语言模型(例如BERT)的出现,导致现有通道检索系统有了重大改进。然而,在中国领域,特别是具体领域,由于质量附加说明的数据集受到规模的限制,通过检索系统仍然不成熟。因此,在本文件中,我们为通过检索提供了中国新的多域数据集(多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多功能/多源。