Paper Info
Title
Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval
Abstract
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.
Year
DOI
Venue
2022
10.1145/3477495.3531736
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Keywords
DocType
Citations 
Passage Retrieval, Chinese Dataset, Human Annotated, Multi Domain
Conference
0
PageRank 
References 
Authors
0.34
11
10
Name
Order
Citations
PageRank
Dingkun Long100.34
Qiong Gao200.34
Kuan Zou300.34
Guangwei Xu499.18
Pengjun Xie515.10
Ruijie Guo600.34
Jian Xu730120.18
Guanjun Jiang800.34
Luxi Xing900.34
Ping Yang1000.34