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Dense passage retrieval aims to retrieve the relevant passages of a query from a large corpus based on dense representations (i.e., vectors) of the query and the passages.
Humeau, S.; Shuster, K.; Lachaux, M.-A.; and Weston, J. 2019 · 1905
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Roberta: A robustly optimized bert pretraining approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
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Context-aware sentence/passage term importance estimation for first stage retrieval
Dai, Z.; and Callan, J. 2019 · 1910
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Pre-training tasks for embedding-based large-scale retrieval
Chang, W.-C.; Yu, F. X.; Chang, Y.-W.; Yang, Y.; and Kumar, S. 2020 · 2002
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Overview of the TREC 2019 deep learning track
Craswell, N.; Mitra, B.; Yilmaz, E.; Campos, D.; and Voorhees, E. M. 2020 · 2003
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Dense passage retrieval for open-domain question answering
Karpukhin, V.; Oğuz, B.; Min, S.; Lewis, P.; Wu, L.; Edunov, S.; Chen, D.; and Yih, W.-t. 2020 · 2004
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RepBERT: Contextualized text embeddings for first-stage retrieval
Zhan, J.; Mao, J.; Liu, Y.; Zhang, M.; and Ma, S. 2020 · 2006
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Approximate nearest neighbor negative contrastive learning for dense text retrieval
Xiong, L.; Xiong, C.; Li, Y.; Tang, K.-F.; Liu, J.; Bennett, P.; Ahmed, J.; and Overwijk, A. 2020 · 2007
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Generation-augmented retrieval for open-domain question answering
Mao, Y.; He, P.; Liu, X.; Shen, Y.; Gao, J.; Han, J.; and Chen, W. 2020 · 2009
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The probabilistic relevance framework: BM25 and beyond
Robertson, S.; Zaragoza, H.; et al. 2009 · 2009
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Neural passage retrieval with improved negative contrast
Lu, J.; Ábrego, G. H.; Ma, J.; Ni, J.; and Yang, Y. 2020 · 2010
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Qu, Y.; Ding, Y.; Liu, J.; Liu, K.; Ren, R.; Zhao, W. X.; Dong, D.; Wu, H.; and Wang, H. 2020 · 2010
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Learning dense representations of phrases at scale
Lee, J.; Sung, M.; Kang, J.; and Chen, D. 2020 · 2012
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MS MARCO: A human generated machine reading comprehension dataset
Nguyen, T.; Rosenberg, M.; Song, X.; Gao, J.; Tiwary, S.; Majumder, R.; and Deng, L. 2016 · 2016
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
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Natural questions: a benchmark for question answering research
Kwiatkowski, T.; Palomaki, J.; Redfield, O.; Collins, M.; Parikh, A.; Alberti, C.; Epstein, D.; Polosukhin, I.; Devlin, J.; Lee, K.; et al. 2019 · 2019
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From doc2query to docTTTTTquery
Nogueira, R.; and Lin, J. 2019 · 2019
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Colbert: Efficient and effective passage search via contextualized late interaction over bert
Khattab, O.; and Zaharia, M. 2020 · 2020
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Colbertv2: Effective and efficient retrieval via lightweight late interaction
Santhanam, K.; Khattab, O.; Saad-Falcon, J.; Potts, C.; and Zaharia, M. 2021 · 2021
Later among the works it cites.
Few-shot conversational dense retrieval
Yu, S.; Liu, Z.; Xiong, C.; Feng, T.; and Liu, Z. 2021 · 2021
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Optimizing dense retrieval model training with hard negatives
Zhan, J.; Mao, J.; Liu, Y.; Guo, J.; Zhang, M.; and Ma, S. 2021 · 2021
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Adversarial retriever-ranker for dense text retrieval
Zhang, H.; Gong, Y.; Shen, Y.; Lv, J.; Duan, N.; and Chen, W. 2021 · 2021
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Adaptive information seeking for open-domain question answering
Zhu, Y.; Pang, L.; Lan, Y.; Shen, H.; and Cheng, X. 2021 · 2021
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Fan, Y.; Xie, X.; Cai, Y.; Chen, J.; Ma, X.; Li, X.; Zhang, R.; Guo, J.; and Liu, Y. 2021 · 2021
Cited alongside, same era.
COIL: Revisit exact lexical match in information retrieval with contextualized inverted list
Gao, L.; Dai, Z.; and Callan, J. 2021 · 2021
Cited alongside, same era.
Efficiently teaching an effective dense retriever with balanced topic aware sampling
Hofstätter, S.; Lin, S.-C.; Yang, J.-H.; Lin, J.; and Hanbury, A. 2021 · 2021
Cited alongside, same era.
Align before fuse: Vision and language representation learning with momentum distillation
Li, J.; Selvaraju, R.; Gotmare, A.; Joty, S.; Xiong, C.; and Hoi, S. C. H. 2021 · 2021
Cited alongside, same era.
Pretrained transformers for text ranking: Bert and beyond
Lin, J.; Nogueira, R.; and Yates, A. 2021 · 2021
Cited alongside, same era.
In-batch negatives for knowledge distillation with tightly-coupled teachers for dense retrieval
Lin, S.-C.; Yang, J.-H.; and Lin, J. 2021 · 2021
Cited alongside, same era.
Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder
Lu, S.; He, D.; Xiong, C.; Ke, G.; Malik, W.; Dou, Z.; Bennett, P.; Liu, T.-Y.; and Overwijk, A. 2021 · 2021
Cited alongside, same era.
Semantic models for the first-stage retrieval: A comprehensive review
Guo, J.; Cai, Y.; Fan, Y.; Sun, F.; Zhang, R.; and Cheng, X. 2022 · 2022
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Masked autoencoders are scalable vision learners
He, K.; Chen, X.; Xie, S.; Li, Y.; Dollár, P.; and Girshick, R. 2022 · 2022
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Li, J.; Li, D.; Xiong, C.; and Hoi, S. 2022 · 2022
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RetroMAE: Pre-training Retrieval-oriented Transformers via Masked Auto-Encoder
Liu, Z.; and Shao, Y. 2022 · 2022
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Pre-train a Discriminative Text Encoder for Dense Retrieval via Contrastive Span Prediction
Ma, X.; Guo, J.; Zhang, R.; Fan, Y.; and Cheng, X. 2022 · 2022
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Should You Mask 15% in Masked Language Modeling?
Wettig, A.; Gao, T.; Zhong, Z.; and Chen, D. 2022 · 2022
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HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Reranking
Zhang, Y.; Long, D.; Xu, G.; and Xie, P. 2022 · 2022
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