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Most dense retrieval models contain an implicit assumption: the training query-document pairs are exactly matched.
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
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Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin. 1977 · 1977
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Towards robust dense retrieval via local ranking alignment
Xuanang Chen, Jian Luo, Ben He, Le Sun, and Yingfei Sun. 2022 · 1986
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020a · 2004
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Neural code search revisited: Enhancing code snippet retrieval through natural language intent
Geert Heyman and Tom Van Cutsem. 2020 · 2008
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Is retriever merely an approximator of reader?
Sohee Yang and Minjoon Seo. 2020 · 2010
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A survey of label-noise representation learning: Past, present and future
Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W Tsang, James T Kwok, and Masashi Sugiyama. 2020 · 2011
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Distilling knowledge from reader to retriever for question answering
Gautier Izacard and Edouard Grave. 2020 · 2012
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Learning semantic representations using convolutional neural networks for web search
Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. 2014 · 2014
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Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and P Shanti Sastry. 2017 · 2017
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer. 2017 · 2017
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Anserini: Enabling the use of lucene for information retrieval research
Peilin Yang, Hui Fang, and Jimmy Lin. 2017 · 2017
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. 2018 · 2018
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A comprehensive study of staqc for deep code summarization
Jayavardhan Reddy Peddamail, Ziyu Yao, Zhen Wang, and Huan Sun. 2018 · 2018
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Staqc: A systematically mined question-code dataset from stack overflow
Ziyu Yao, Daniel S Weld, Wei-Peng Chen, and Huan Sun. 2018 · 2018
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Google dataset search: Building a search engine for datasets in an open web ecosystem
Dan Brickley, Matthew Burgess, and Natasha Noy. 2019 · 2019
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Pre-training tasks for embedding-based large-scale retrieval
Wei-Cheng Chang, X Yu Felix, Yin-Wen Chang, Yiming Yang, and Sanjiv Kumar. 2019 · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
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.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Cited alongside, same era.
Latent retrieval for weakly supervised open domain question answering
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
End-to-end training of neural retrievers for open-domain question answering
Devendra Singh Sachan, Mostofa Patwary, Mohammad Shoeybi, Neel Kant, Wei Ping, William L. Hamilton, and Bryan Catanzaro. 2021 · 2021
Later among the works it cites.
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. 2021 · 2021
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Poolingformer: Long document modeling with pooling attention
Hang Zhang, Yeyun Gong, Yelong Shen, Weisheng Li, Jiancheng Lv, Nan Duan, and Weizhu Chen. 2021 · 2021
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Precise zero-shot dense retrieval without relevance labels
Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan. 2022 · 2022
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Unixcoder: Unified cross-modal pre-training for code representation
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022 · 2022
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Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. 2020 · 2020
Cited alongside, same era.
Normalized loss functions for deep learning with noisy labels
Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, and James Bailey. 2020 · 2020
Cited alongside, same era.
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, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
Cited alongside, same era.
Understanding and improving early stopping for learning with noisy labels
Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, and Tongliang Liu. 2021 · 2021
Cited alongside, same era.
Graphcodebert: Pre-training code representations with data flow
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin B. Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou. 2021 · 2021
Cited alongside, same era.
Learning with noisy correspondence for cross-modal matching
Zhenyu Huang, Guocheng Niu, Xiao Liu, Wenbiao Ding, Xinyan Xiao, Hua Wu, and Xi Peng. 2021 · 2021
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. 2021 · 2021
Cited alongside, same era.
Xiaonan Li, Yeyun Gong, Yelong Shen, Xipeng Qiu, Hang Zhang, Bolun Yao, Weizhen Qi, Daxin Jiang, Weizhu Chen, and Nan Duan. 2022 · 2022
Later among the works it cites.
Few-shot learning with noisy labels
Kevin J Liang, Samrudhdhi B. Rangrej, Vladan Petrovic, and Tal Hassner. 2022 · 2022
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Pre-train a discriminative text encoder for dense retrieval via contrastive span prediction
Xinyu Ma, Jiafeng Guo, Ruqing Zhang, Yixing Fan, and Xueqi Cheng. 2022 · 2022
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Questions are all you need to train a dense passage retriever
Devendra Singh Sachan, Mike Lewis, Dani Yogatama, Luke Zettlemoyer, Joelle Pineau, and Manzil Zaheer. 2022 · 2022
Later among the works it cites.
Noise-robust learning from multiple unsupervised sources of inferred labels
Amila Silva, Ling Luo, Shanika Karunasekera, and Christopher Leckie. 2022 · 2022
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Text embeddings by weakly-supervised contrastive pre-training
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022 · 2022
Later among the works it cites.
Curriculum learning for dense retrieval distillation
Hansi Zeng, Hamed Zamani, and Vishwa Vinay. 2022 · 2022
Later among the works it cites.
Noisy correspondence learning with meta similarity correction
Haochen Han, Kaiyao Miao, Qinghua Zheng, and Minnan Luo. 2023 · 2023
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Annollm: Making large language models to be better crowdsourced annotators
Xingwei He, Zhenghao Lin, Yeyun Gong, Alex Jin, Hang Zhang, Chen Lin, Jian Jiao, Siu Ming Yiu, Nan Duan, Weizhu Chen, et al. 2023 · 2023
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Unifier: A unified retriever for large-scale retrieval
Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Guodong Long, Kai Zhang, and Daxin Jiang. 2023 · 2023
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Led: Lexicon-enlightened dense retriever for large-scale retrieval
Kai Zhang, Chongyang Tao, Tao Shen, Can Xu, Xiubo Geng, Binxing Jiao, and Daxin Jiang. 2023 · 2023
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