DuReader-Retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine

Yifu Qiu, Hongyu Li, Yingqi Qu, Ying Chen, QiaoQiao She, Jing Liu, Hua Wu, Haifeng Wang


Abstract
In this paper, we present DuReader-retrieval, a large-scale Chinese dataset for passage retrieval. DuReader-retrieval contains more than 90K queries and over 8M unique passages from a commercial search engine. To alleviate the shortcomings of other datasets and ensure the quality of our benchmark, we (1) reduce the false negatives in development and test sets by manually annotating results pooled from multiple retrievers, and (2) remove the training queries that are semantically similar to the development and testing queries. Additionally, we provide two out-of-domain testing sets for cross-domain evaluation, as well as a set of human translated queries for for cross-lingual retrieval evaluation. The experiments demonstrate that DuReader-retrieval is challenging and a number of problems remain unsolved, such as the salient phrase mismatch and the syntactic mismatch between queries and paragraphs. These experiments also show that dense retrievers do not generalize well across domains, and cross-lingual retrieval is essentially challenging. DuReader-retrieval is publicly available at https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval.
Anthology ID:
2022.emnlp-main.357
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5326–5338
Language:
URL:
https://aclanthology.org/2022.emnlp-main.357
DOI:
10.18653/v1/2022.emnlp-main.357
Bibkey:
Cite (ACL):
Yifu Qiu, Hongyu Li, Yingqi Qu, Ying Chen, QiaoQiao She, Jing Liu, Hua Wu, and Haifeng Wang. 2022. DuReader-Retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5326–5338, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
DuReader-Retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine (Qiu et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.357.pdf