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Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve the performance of first-stage retrieval in IR systems.
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Overview of the TREC 2020 Deep Learning Track
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Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking
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Colbert: Efficient and effective passage search via contextualized late interaction over bert. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval . 39–48
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Distilling dense representations for ranking using tightly-coupled teachers
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RepBERT: Contextualized text embeddings for first-stage retrieval
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Complement lexical retrieval model with semantic residual embeddings. In European Conference on Information Retrieval . Springer, 146–160
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A Neural Corpus Indexer for Document Retrieval
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THUIR at the NTCIR-16 WWW-4 Task
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Enhance Performance of Ad-hoc Search via Prompt Learning. In Information Retrieval: 28th China Conference, CCIR 2022, Chongqing, China, September 16–18, 2022, Revised Selected Papers . Springer, 28–39
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Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining . 1328–1336
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THUIR at WSDM Cup 2023 Task 1: Unbiased Learning to Rank
Jia Chen, Haitao Li, Weihang Su, Qingyao Ai, and Yiqun Liu. [n. d.] · 2023
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Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank
Haitao Li, Jia Chen, Weihang Su, Qingyao Ai, and Yiqun Liu. 2023 · 2023
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T2Ranking: A large-scale Chinese Benchmark for Passage Ranking
Xiaohui Xie, Qian Dong, Bingning Wang, Feiyang Lv, Ting Yao, Weinan Gan, Zhijing Wu, Xiangsheng Li, Haitao Li, Yiqun Liu, et al · 2023
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