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Open domain question answering (ODQA) is a longstanding task aimed at answering factual questions from a large knowledge corpus without any explicit evidence in natural language processing (NLP).
Language models are few-shot learners
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Video google: a text retrieval approach to object matching in videos
Sivic and Zisserman. 2003 · 2003
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Hassan Sajjad, Fahim Dalvi, Nadir Durrani, and Preslav Nakov. 2020 · 2004
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Building watson: An overview of the deepqa project
David Ferrucci, Eric Brown, Jennifer Chu-Carroll, James Fan, David Gondek, Aditya A Kalyanpur, Adam Lally, J William Murdock, Eric Nyberg, John Prager, et al. 2010 · 2010
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Product quantization for nearest neighbor search
Hervé Jégou, Matthijs Douze, and Cordelia Schmid. 2011 · 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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A memory efficient baseline for open domain question answering
Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Sebastian Riedel, and Edouard Grave. 2020 · 2012
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Asymmetric lsh (alsh) for sublinear time maximum inner product search (mips)
Anshumali Shrivastava and Ping Li. 2014 · 2014
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Yodaqa: a modular question answering system pipeline
Petr Baudiš. 2015 · 2015
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Conditional computation in neural networks for faster models
Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau, and Doina Precup. 2015 · 2015
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016 · 2016
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Adaptive computation time for recurrent neural networks
Alex Graves. 2016 · 2016
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Quantization based fast inner product search
Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, and David Simcha. 2016 · 2016
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Hashnet: Deep learning to hash by continuation
Zhangjie Cao, Mingsheng Long, Jianmin Wang, and Philip S. Yu. 2017 · 2017
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Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
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Efficient and robust question answering from minimal context over documents
Sewon Min, Victor Zhong, Richard Socher, and Caiming Xiong. 2018 · 2018
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Gender bias in coreference resolution
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018 · 2018
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A survey on learning to hash
Jingdong Wang, Ting Zhang, jingkuan song, Nicu Sebe, and Heng Tao Shen. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Latent retrieval for weakly supervised open domain question answering
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019 · 2019
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Real-time open-domain question answering with dense-sparse phrase index
Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski, Ankur Parikh, Ali Farhadi, and Hannaneh Hajishirzi. 2019 · 2019
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Evaluating gender bias in machine translation
Gabriel Stanovsky, Noah A. Smith, and Luke Zettlemoyer. 2019 · 2019
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End-to-end open-domain question answering with BERTserini
Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019 · 2019
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Language (technology) is power: A critical survey of “bias” in NLP
Block pruning for faster transformers
François Lagunas, Ella Charlaix, Victor Sanh, and Alexander Rush. 2021 · 2021
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Phrase retrieval learns passage retrieval, too
Jinhyuk Lee, Alexander Wettig, and Danqi Chen. 2021c · 2021
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PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
Patrick Lewis, Yuxiang Wu, Linqing Liu, Pasquale Minervini, Heinrich Küttler, Aleksandra Piktus, Pontus Stenetorp, and Sebastian Riedel. 2021 · 2021
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Sparse, Dense, and Attentional Representations for Text Retrieval
Yi Luan, Jacob Eisenstein, Kristina Toutanova, and Michael Collins. 2021 · 2021
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Simple and effective unsupervised redundancy elimination to compress dense vectors for passage retrieval
Xueguang Ma, Minghan Li, Kai Sun, Ji Xin, and Jimmy Lin. 2021 · 2021
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Generation-augmented retrieval for open-domain question answering
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020 · 2020
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A survey of methods for low-power deep learning and computer vision
Abhinav Goel, Caleb Tung, Yung-Hsiang Lu, and George K. Thiruvathukal. 2020 · 2020
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Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020 · 2020
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Recent trends in deep learning based open-domain textual question answering systems
Zhen Huang, Shiyi Xu, Minghao Hu, Xinyi Wang, Jinyan Qiu, Yongquan Fu, Yuncai Zhao, Yuxing Peng, and Changjian Wang. 2020 · 2020
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Dense passage retrieval for open-domain question answering
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Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, and Weizhu Chen. 2021 · 2021
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RocketQA: An optimized training approach to dense passage retrieval for open-domain question answering
Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. 2021 · 2021
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Training adaptive computation for open-domain question answering with computational constraints
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Designing a minimal retrieve-and-read system for open-domain question answering
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Jointly optimizing query encoder and product quantization to improve retrieval performance
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Retrieving and reading: A comprehensive survey on open-domain question answering
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Enable deep learning on mobile devices: Methods, systems, and applications
Han Cai, Ji Lin, Yujun Lin, Zhijian Liu, Haotian Tang, Hanrui Wang, Ligeng Zhu, and Song Han. 2022 · 2022
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