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Open-Domain Question Answering (ODQA) systems necessitate a reader model capable of generating answers by simultaneously referring to multiple passages.
Leveraging passage retrieval with generative models for open domain question answering
Gautier Izacard and Edouard Grave. 2020b · 2007
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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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Measuring the tendency of cnns to learn surface statistical regularities
Jason Jo and Yoshua Bengio. 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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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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Learning the difference that makes a difference with counterfactually augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary C Lipton. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Fooling lime and shap: Adversarial attacks on post hoc explanation methods
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju. 2020 · 2020
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Robustness to spurious correlations in text classification via automatically generated counterfactuals
Zhao Wang and Aron Culotta. 2020 · 2020
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Counterfactual generator: A weakly-supervised method for named entity recognition
Xiangji Zeng, Yunliang Li, Yuchen Zhai, and Yin Zhang. 2020 · 2020
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R2-D2: A modular baseline for open-domain question answering
Martin Fajcik, Martin Docekal, Karel Ondrej, and Pavel Smrz. 2021 · 2021
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Explaining the efficacy of counterfactually augmented data
Divyansh Kaushik, Amrith Setlur, Eduard Hovy, and Zachary C Lipton. 2021 · 2021
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Pretrain knowledge-aware language models
Autoregressive search engines: Generating substrings as document identifiers
Michele Bevilacqua, Giuseppe Ottaviano, Patrick Lewis, Wen tau Yih, Sebastian Riedel, and Fabio Petroni. 2022 · 2022
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Grape: Knowledge graph enhanced passage reader for open-domain question answering
Mingxuan Ju, Wenhao Yu, Tong Zhao, Chuxu Zhang, and Yanfang Ye. 2022 · 2022
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A rationale-centric framework for human-in-the-loop machine learning
Jinghui Lu, Linyi Yang, Brian Mac Namee, and Yue Zhang. 2022 · 2022
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Generating data to mitigate spurious correlations in natural language inference datasets
Yuxiang Wu, Matt Gardner, Pontus Stenetorp, and Pradeep Dasigi. 2022 · 2022
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Factmix: Using a few labeled in-domain examples to generalize to cross-domain named entity recognition
Linyi Yang, Lifan Yuan, Leyang Cui, Wenyang Gao, and Yue Zhang. 2022 · 2022
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Distilling knowledge from reader to retriever for question answering
Gautier Izacard and Edouard Grave. 2020a
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KG-FiD: Infusing knowledge graph in fusion-in-decoder for open-domain question answering
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