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Ranking has always been one of the top concerns in information retrieval researches.
Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In SIGIR’94 . Springer, 232–241
Stephen E Robertson and Steve Walker. 1994 · 1994
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Learning to rank for information retrieval
Tie-Yan Liu. 2009 · 2009
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From ranknet to lambdarank to lambdamart: An overview
Christopher JC Burges. 2010 · 2010
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Noise-contrastive estimation: a new estimation principle for unnormalized statistical models. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics . 297–304
Michael Gutmann and Aapo Hyvärinen. 2010 · 2010
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Product quantization for nearest neighbor search
Herve Jegou, Matthijs Douze, and Cordelia Schmid. 2010 · 2010
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A general approximation framework for direct optimization of information retrieval measures
Tao Qin, Tie-Yan Liu, and Hang Li. 2010 · 2010
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013 · 2013
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Semantic matching in search
Hang Li and Jun Xu. 2014 · 2014
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Learning binary codes for maximum inner product search. In Proceedings of the IEEE International Conference on Computer Vision . 4148–4156
Fumin Shen, Wei Liu, Shaoting Zhang, Yang Yang, and Heng Tao Shen. 2015 · 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
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
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Attention is All You Need. In Advances in neural information processing systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Anserini: Reproducible ranking baselines using Lucene
Peilin Yang, Hui Fang, and Jimmy Lin. 2018 · 2018
Cited alongside, same era.
Overview of the TREC 2019 deep learning track. In Text REtrieval Conference (TREC) . TREC
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M. Voorhees. 2020 · 2019
Cited alongside, same era.
Context-aware sentence/passage term importance estimation for first stage retrieval
Zhuyun Dai and Jamie Callan. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 4171–4186
Yingqi Qu Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. 2020 · 2020
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Complementing lexical retrieval with semantic residual embedding
Luyu Gao, Zhuyun Dai, Zhen Fan, and Jamie Callan. 2020 · 2020
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Realm: retrieval-augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2020
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Learning-to-Rank with BERT in TF-Ranking
Shuguang Han, Xuanhui Wang, Mike Bendersky, and Marc Najork. 2020 · 2020
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Billion-scale similarity search with GPUs
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019 · 2019
Cited alongside, same era.
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 · 2019
Cited alongside, same era.
Rodrigo Nogueira and Kyunghyun Cho. 2019 · 2019
Cited alongside, same era.
Document expansion by query prediction
Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho. 2019 · 2019
Cited alongside, same era.
IDST at TREC 2019 Deep Learning Track: Deep Cascade Ranking with Generation-based Document Expansion and Pre-trained Language Modeling.. In TREC
Ming Yan, Chenliang Li, Chen Wu, Bin Bi, Wei Wang, Jiangnan Xia, and Luo Si. 2019 · 2019
Cited alongside, same era.
Large batch optimization for deep learning: Training bert in 76 minutes
Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, and Cho-Jui Hsieh. 2019 · 2019
Cited alongside, same era.
Embedding-based Retrieval in Facebook Search. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2553–2561
Jui-Ting Huang, Ashish Sharma, Shuying Sun, Li Xia, David Zhang, Philip Pronin, Janani Padmanabhan, Giuseppe Ottaviano, and Linjun Yang. 2020 · 2020
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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 · 2020
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ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
O. Khattab and M. Zaharia. 2020 · 2020
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Distilling Dense Representations for Ranking using Tightly-Coupled Teachers
Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. 2020 · 2020
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Sparse, dense, and attentional representations for text retrieval
Yi Luan, Jacob Eisenstein, Kristina Toutanove, and Michael Collins. 2020 · 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 · 2020
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RepBERT: Contextualized Text Embeddings for First-Stage Retrieval
Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, and Shaoping Ma. 2020 · 2020
Later among the works it cites.