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The computing cost of transformer self-attention often necessitates breaking long documents to fit in pretrained models in document ranking tasks.
Rodrigo Nogueira and Kyunghyun Cho. 2019 · 1901
Earlier work this paper cites.
Simple applications of bert for ad hoc document retrieval
Wei Yang, Haotian Zhang, and Jimmy Lin. 2019 · 1903
Earlier work this paper cites.
Bert rediscovers the classical nlp pipeline
Ian Tenney, Dipanjan Das, and Ellie Pavlick. 2019 · 1905
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Multi-stage document ranking with bert
Rodrigo Nogueira, Wei Yang, Kyunghyun Cho, and Jimmy Lin. 2019 · 1910
Earlier work this paper cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
Earlier work this paper cites.
Tu wien@ trec deep learning’19–simple contextualization for re-ranking
Sebastian Hofstätter, Markus Zlabinger, and Allan Hanbury. 2019 · 1912
Earlier work this paper cites.
Passage-level evidence in document retrieval
James P Callan. 1994 · 1994
Earlier work this paper cites.
Reformer: The efficient transformer
Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya. 2020 · 2001
Earlier work this paper cites.
Interpretable & time-budget-constrained contextualization for re-ranking
Sebastian Hofstätter, Markus Zlabinger, and Allan Hanbury. 2020 · 2002
Earlier work this paper cites.
Overview of the trec 2019 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M Voorhees. 2020 · 2003
Earlier work this paper cites.
Longformer: The long-document transformer
Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020 · 2004
Earlier work this paper cites.
A markov random field model for term dependencies
Donald Metzler and W Bruce Croft. 2005 · 2005
Earlier work this paper cites.
Linear feature-based models for information retrieval
Donald Metzler and W Bruce Croft. 2007 · 2007
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Ms marco: A human generated machine reading comprehension dataset
Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, et al. 2016 · 2016
Cited alongside, same era.
A deep relevance matching model for ad-hoc retrieval
Jiafeng Guo, Yixing Fan, Qingyao Ai, and W. Bruce Croft. 2016 · 2016
Cited alongside, same era.
Hierarchical attention networks for document classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016 · 2016
Cited alongside, same era.
Pacrr: A position-aware neural ir model for relevance matching
Kai Hui, Andrew Yates, Klaus Berberich, and Gerard de Melo. 2017 · 2017
Cited alongside, same era.
Deeper text understanding for ir with contextual neural language modeling
Zhuyun Dai and Jamie Callan. 2019 · 2019
Later among the works it cites.
Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, and Ruslan Salakhutdinov. 2019 · 2019
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
A deep look into neural ranking models for information retrieval
Jiafeng Guo, Yixing Fan, Liang Pang, Liu Yang, Qingyao Ai, Hamed Zamani, Chen Wu, W. Bruce Croft, and Xueqi Cheng. 2019 · 2019
Later among the works it cites.
Semantic text matching for long-form documents
Jyun-Yu Jiang, Mingyang Zhang, Cheng Li, Michael Bendersky, Nadav Golbandi, and Marc Najork. 2019 · 2019
Later among the works it cites.
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Chenyan Xiong, Zhuyun Dai, Jamie Callan, Zhiyuan Liu, and Russell Power. 2017 · 2017
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{ \{ TVM } \} : An automated end-to-end optimizing compiler for deep learning
Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, et al. 2018 · 2018
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Convolutional neural networks for soft-matching n-grams in ad-hoc search
Zhuyun Dai, Chenyan Xiong, Jamie Callan, and Zhiyuan Liu. 2018 · 2018
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Zeynep Akkalyoncu Yilmaz, Shengjin Wang, and Jimmy Lin. 2019 · 2019
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Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. 2019 · 2019
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Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
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Adaptive attention span in transformers
Sainbayar Sukhbaatar, Edouard Grave, Piotr Bojanowski, and Armand Joulin. 2019 · 2019
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Idst at trec 2019 deep learning track: Deep cascade ranking with generation-based document expansion and pre-trained language modeling
Ming Yan, Chenliang Li, Chen Wu, Bin Bi, Wei Wang, Jiangnan Xia, and Luo Si. 2020 · 2019
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Reproducing and generalizing semantic term matching in axiomatic information retrieval
Peilin Yang and Jimmy Lin. 2019 · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
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Local self-attention over long text for efficient document retrieval
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Selective weak supervision for neural information retrieval
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