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Dense retrieval overcome the lexical gap and has shown great success in ad-hoc information retrieval (IR).
1907
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1910
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Product quantization for nearest neighbor search,
H. Jegou, M. Douze, C. Schmid, · 2010
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2010
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2012
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Approximate nearest neighbor algorithm based on navigable small world graphs,
Y. Malkov, A. Ponomarenko, A. Logvinov, V. Krylov, · 2013
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Optimized product quantization,
T. Ge, K. He, Q. Ke, J. Sun, · 2013
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Ms marco: A human generated machine reading comprehension dataset,
T. Nguyen, M. Rosenberg, X. Song, J. Gao, S. Tiwary, R. Majumder, L. Deng, · 2016
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Dbpedia-entity v2: A test collection for entity search,
F. Hasibi, F. Nikolaev, C. Xiong, K. Balog, S. E. Bratsberg, A. Kotov, J. Callan, · 2017
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Latent retrieval for weakly supervised open domain question answering,
K. Lee, M.-W. Chang, K. Toutanova, · 2019
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks,
N. Reimers, I. Gurevych, · 2019
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Learning dense representations for entity retrieval,
D. Gillick, S. Kulkarni, L. Lansing, A. Presta, J. Baldridge, E. Ie, D. Garcia-Olano, · 2019
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Billion-scale similarity search with GPUs,
J. Johnson, M. Douze, H. Jégou, · 2019
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Representation learning with contrastive predictive coding,
A. van den Oord, Y. Li, O. Vinyals, · 2019
Cited alongside, same era.
R. Nogueira, J. Lin, From doc2query to docTTTTTquery, 2019. URL: https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf
2019
Cited alongside, same era.
Dense passage retrieval for open-domain question answering,
V. Karpukhin, B. Oguz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, W.-t. Yih, · 2020
Cited alongside, same era.
O. Khattab, M. Zaharia, ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT, Association for Computing Machinery, New York, NY, USA, 2020, p. 39–48. URL: https://doi.org/10.1145/3397271.3401075
2020
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Transformers: State-of-the-art natural language processing,
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. Le Scao, S. Gugger, M. Drame, Q. Lhoest, A. Rush, · 2020
Efficient passage retrieval with hashing for open-domain question answering,
I. Yamada, A. Asai, H. Hajishirzi, · 2021
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Simple and effective unsupervised redundancy elimination to compress dense vectors for passage retrieval,
X. Ma, M. Li, K. Sun, J. Xin, J. Lin, · 2021
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Jointly optimizing query encoder and product quantization to improve retrieval performance,
J. Zhan, J. Mao, Y. Liu, J. Guo, M. Zhang, S. Ma, · 2021
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BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models,
N. Thakur, N. Reimers, A. Rücklé, A. Srivastava, I. Gurevych, · 2021
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2021
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Cited alongside, same era.
S. Yu, Z. Liu, C. Xiong, T. Feng, Z. Liu, Few-Shot Conversational Dense Retrieval, Association for Computing Machinery, New York, NY, USA, 2021, p. 829–838. URL: https://doi.org/10.1145/3404835.3462856
2021
Cited alongside, same era.
Improving evidence retrieval for automated explainable fact-checking,
C. Samarinas, W. Hsu, M. L. Lee, · 2021
Cited alongside, same era.
Complement lexical retrieval model with semantic residual embeddings,
L. Gao, Z. Dai, T. Chen, Z. Fan, B. Van Durme, J. Callan, · 2021
Cited alongside, same era.
Approximate nearest neighbor negative contrastive learning for dense text retrieval,
L. Xiong, C. Xiong, Y. Li, K.-F. Tang, J. Liu, P. N. Bennett, J. Ahmed, A. Overwijk, · 2021
Cited alongside, same era.
S. Hofstätter, S.-C. Lin, J.-H. Yang, J. Lin, A. Hanbury, Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling, Association for Computing Machinery, New York, NY, USA, 2021, p. 113–122. URL: https://doi.org/10.1145/3404835.3462891
2021
Cited alongside, same era.
J. Zhan, J. Mao, Y. Liu, J. Guo, M. Zhang, S. Ma, Optimizing Dense Retrieval Model Training with Hard Negatives, Association for Computing Machinery, New York, NY, USA, 2021, p. 1503–1512. URL: https://doi.org/10.1145/3404835.3462880
2021
Cited alongside, same era.
Sparse, Dense, and Attentional Representations for Text Retrieval,
Y. Luan, J. Eisenstein, K. Toutanova, M. Collins, · 2021
Cited alongside, same era.
Optimizing dense retrieval model training with hard negatives,
J. Zhan, J. Mao, Y. Liu, J. Guo, M. Zhang, S. Ma, · 2021
Later among the works it cites.
GPL: Generative pseudo labeling for unsupervised domain adaptation of dense retrieval,
K. Wang, N. Thakur, N. Reimers, I. Gurevych, · 2022
Closest in time.
Zero-shot dense retrieval with momentum adversarial domain invariant representations,
J. Xin, C. Xiong, A. Srinivasan, A. Sharma, D. Jose, P. Bennett, · 2022
Closest in time.
TLDR: Twin learning for dimensionality reduction,
Y. Kalantidis, C. E. R. K. Lassance, J. Almazán, D. Larlus, · 2022
Closest in time.
J. Zhan, J. Mao, Y. Liu, J. Guo, M. Zhang, S. Ma, Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval, Association for Computing Machinery, New York, NY, USA, 2022, p. 1328–1336. URL: https://doi.org/10.1145/3488560.3498443
2022
Closest in time.
Boosted dense retriever,
P. Lewis, B. Oguz, W. Xiong, F. Petroni, S. Yih, S. Riedel, · 2022
Closest in time.
Unsupervised dense information retrieval with contrastive learning,
G. Izacard, M. Caron, L. Hosseini, S. Riedel, P. Bojanowski, A. Joulin, E. Grave, · 2022
Closest in time.