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This work proposes a novel adaptation of a pretrained sequence-to-sequence model to the task of document ranking.
Okapi at TREC-3
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CEDR: Contextualized embeddings for document ranking
S. MacAvaney, A. Yates, A. Cohan, and N. Goharian · 2019
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R. Nogueira and K. Cho · 2019
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Multi-stage document ranking with BERT
R. Nogueira, W. Yang, K. Cho, and J. Lin · 2019
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Zero-shot text classification with generative language models
R. Puri and B. Catanzaro · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu · 2019
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Critically examining the “neural hype” weak baselines and the additivity of effectiveness gains from neural ranking models
W. Yang, K. Lu, P. Yang, and J. Lin · 2019
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Cross-domain modeling of sentence-level evidence for document retrieval
Z. A. Yilmaz, W. Yang, H. Zhang, and J. Lin · 2019
Later among the works it cites.
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R. Nogueira, W. Yang, J. Lin, and K. Cho · 2019
Cited alongside, same era.
J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei · 2020
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Rethinking query expansion for BERT reranking
R. Padaki, Z. Dai, and J. Callan · 2020
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