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Passage ranking involves two stages: passage retrieval and passagere-ranking, which are important and challenging topics for both academics andindustries in the area of Information Retrieval (IR). However, thecommonly-used datasets for passage ranking usually focus on the Englishlanguage. For non-English scenarios, such as Chinese, the existing datasets arelimited in terms of data scale, fine-grained relevance annotation and falsenegative issues. To address this problem, we introduce T2Ranking, a large-scaleChinese benchmark for passage ranking. T2Ranking comprises more than 300Kqueries and over 2M unique passages from real-world search engines. Expertannotators are recruited to provide 4-level graded relevance scores(fine-grained) for query-passage pairs instead of binary relevance judgments(coarse-grained). To ease the false negative issues, more passages with higherdiversities are considered when performing relevance annotations, especially inthe test set, to ensure a more accurate evaluation. Apart from the textualquery and passage data, other auxiliary resources are also provided, such asquery types and XML files of documents which passages are generated from, tofacilitate further studies. To evaluate the dataset, commonly used rankingmodels are implemented and tested on T2Ranking as baselines. The experimentalresults show that T2Ranking is challenging and there is still scope forimprovement. The full data and all codes are available athttps://github.com/THUIR/T2Ranking/

T2Ranking: A large-scale Chinese Benchmark for Passage Ranking