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The Transformer-Kernel (TK) model has demonstrated strong reranking performance on the TREC Deep Learning benchmark---and can be considered to be an efficient (but slightly less effective) alternative to BERT-based ranking models.
An updated duet model for passage re-ranking
Bhaskar Mitra and Nick Craswell · 1903
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Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 1904
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Document expansion by query prediction
Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho · 1904
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Cumulated gain-based evaluation of ir techniques
Kalervo Järvelin and Jaana Kekäläinen · 2002
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Learning to rank using gradient descent
Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender · 2005
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Mean reciprocal rank
Nick Craswell · 2009
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The probabilistic relevance framework: Bm25 and beyond
Stephen Robertson, Hugo Zaragoza, et al · 2009
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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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
Earlier work this paper cites.
Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov · 2016
Earlier work this paper cites.
A dual embedding space model for document ranking
Bhaskar Mitra, Eric Nalisnick, Nick Craswell, and Rich Caruana · 2016
Earlier work this paper cites.
Improving document ranking with dual word embeddings
Eric Nalisnick, Bhaskar Mitra, Nick Craswell, and Rich Caruana · 2016
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Neural ranking models with weak supervision
Mostafa Dehghani, Hamed Zamani, Aliaksei Severyn, Jaap Kamps, and W Bruce Croft · 2017
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Learning to match using local and distributed representations of text for web search
Bhaskar Mitra, Fernando Diaz, and Nick Craswell · 2017
Earlier work this paper cites.
Benchmark for complex answer retrieval
Federico Nanni, Bhaskar Mitra, Matt Magnusson, and Laura Dietz · 2017
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Task-oriented query reformulation with reinforcement learning
Rodrigo Nogueira and Kyunghyun Cho · 2017
Earlier work this paper cites.
Reply with: Proactive recommendation of email attachments
Christophe Van Gysel, Bhaskar Mitra, Matteo Venanzi, Roy Rosemarin, Grzegorz Kukla, Piotr Grudzien, and Nicola Cancedda · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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The case for learned index structures
Tim Kraska, Alex Beutel, Ed H Chi, Jeffrey Dean, and Neoklis Polyzotis · 2018
Earlier work this paper cites.
An introduction to neural information retrieval
Bhaskar Mitra and Nick Craswell · 2018
Cited alongside, same era.
The potential of learned index structures for index compression
Harrie Oosterhuis, J Shane Culpepper, and Maarten de Rijke · 2018
Cited alongside, same era.
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Łukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran · 2018
Cited alongside, same era.
Optimizing query evaluations using reinforcement learning for web search
Corby Rosset, Damien Jose, Gargi Ghosh, Bhaskar Mitra, and Saurabh Tiwary · 2018
Cited alongside, same era.
Attention? attention!
Lilian Weng · 2018
Cited alongside, same era.
From neural re-ranking to neural ranking: Learning a sparse representation for inverted indexing
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 · 2019
Later among the works it cites.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le · 2019
Later among the works it cites.
H2oloo at trec 2019: Combining sentence and document evidence in the deep learning track
Zeynep Akkalyoncu Yilmaz, Shengjin Wang, and Jimmy Lin · 2019
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Longformer: The long-document transformer
Iz Beltagy, Matthew E. Peters, and Arman Cohan · 2020
Closest in time.
Pre-training tasks for embedding-based large-scale retrieval
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Hamed Zamani, Mostafa Dehghani, W Bruce Croft, Erik Learned-Miller, and Jaap Kamps · 2018
Cited alongside, same era.
Reqa: An evaluation for end-to-end answer retrieval models
Amin Ahmad, Noah Constant, Yinfei Yang, and Daniel Cer · 2019
Cited alongside, same era.
Overview of the trec 2019 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, and Daniel Campos · 2019
Cited alongside, same era.
An evaluation of weakly-supervised deepct in the trec 2019 deep learning track
Zhuyun Dai and Jamie Callan · 2019
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Reformer: The efficient transformer
Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya · 2019
Cited alongside, same era.
Wei-Cheng Chang, Felix X Yu, Yin-Wen Chang, Yiming Yang, and Sanjiv Kumar · 2020
Closest in time.
Orcas: 18 million clicked query-document pairs for analyzing search
Nick Craswell, Daniel Campos, Bhaskar Mitra, Emine Yilmaz, and Bodo Billerbeck · 2020
Closest in time.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
Closest in time.
Colbert: Efficient and effective passage search via contextualized late interaction over bert
Omar Khattab and Matei Zaharia · 2020
Closest in time.
Sparse, dense, and attentional representations for text retrieval
Yi Luan, Jacob Eisenstein, Kristina Toutanova, and Michael Collins · 2020
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Zero-shot neural retrieval via domain-targeted synthetic query generation
Ji Ma, Ivan Korotkov, Yinfei Yang, Keith Hall, and Ryan McDonald · 2020
Closest in time.
Expansion via prediction of importance with contextualization
Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, and Ophir Frieder · 2020
Closest in time.
Efficiency implications of term weighting for passage retrieval
Joel Mackenzie, Zhuyun Dai, Luke Gallagher, and Jamie Callan · 2020
Closest in time.
Efficient content-based sparse attention with routing transformers
Aurko Roy, Mohammad Saffar, Ashish Vaswani, and David Grangier · 2020
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Yi Tay, Dara Bahri, Liu Yang, Donald Metzler, and Da-Cheng Juan · 2020
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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
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The transformer family
Lilian Weng · 2020
Closest in time.
Lite transformer with long-short range attention
Zhanghao Wu, Zhijian Liu, Ji Lin, Yujun Lin, and Song Han · 2020
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Approximate nearest neighbor negative contrastive learning for dense text retrieval
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, and Arnold Overwijk · 2020
Closest in time.
On the reliability of test collections to evaluating systems of different types
Emine Yilmaz, Nick Craswell, Bhaskar Mitra, and Daniel Campos · 2020
Closest in time.