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End-to-end question answering (QA) requires both information retrieval (IR) over a large document collection and machine reading comprehension (MRC) on the retrieved passages.
A bert baseline for the natural questions
Alberti, C.; Lee, K.; and Collins, M. 2019 · 1901
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Synthetic QA corpora generation with roundtrip consistency
Alberti, C.; Andor, D.; Pitler, E.; Devlin, J.; and Collins, M. 2019 · 1906
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Roberta: A robustly optimized bert pretraining approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019b · 1907
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2019 · 1910
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HuggingFace’s Transformers: State-of-the-art Natural Language Processing
Wolf, T.; Debut, L.; Sanh, V.; Chaumond, J.; Delangue, C.; Moi, A.; Cistac, P.; Rault, T.; Louf, R.; Funtowicz, M.; Davison, J.; Shleifer, S.; von Platen, P.; Ma, C.; Jernite, Y.; Plu, J.; Xu, C.; Scao, T. L.; Gugger, S.; Drame, M.; Lhoest, Q.; and Rush, A. M. 2019 · 1910
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Harvesting Paragraph-level Question-Answer Pairs from Wikipedia
Du, X.; and Cardie, C. 2018 · 1917
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Reinforced training data selection for domain adaptation
Liu, M.; Song, Y.; Zou, H.; and Zhang, T. 2019a · 1968
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Realm: Retrieval-augmented language model pre-training
Guu, K.; Lee, K.; Tung, Z.; Pasupat, P.; and Chang, M.-W. 2020 · 2002
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Dense Passage Retrieval for Open-Domain Question Answering
Karpukhin, V.; Oğuz, B.; Min, S.; Wu, L.; Edunov, S.; Chen, D.; and Yih, W.-t. 2020 · 2004
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RikiNet: Reading Wikipedia Pages for Natural Question Answering
Liu, D.; Gong, Y.; Fu, J.; Yan, Y.; Chen, J.; Jiang, D.; Lv, J.; and Duan, N. 2020 · 2004
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Rapidly Bootstrapping a Question Answering Dataset for COVID-19
Tang, R.; Nogueira, R.; Zhang, E.; Gupta, N.; Cam, P.; Cho, K.; and Lin, J. 2020 · 2004
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Paired t test
Hsu, H.; and Lachenbruch, P. A. 2005 · 2005
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Lewis, P.; Perez, E.; Piktus, A.; Petroni, F.; Karpukhin, V.; Goyal, N.; Küttler, H.; Lewis, M.; Yih, W.-t.; Rocktäschel, T.; et al. 2020b · 2005
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Answering Questions on COVID-19 in Real-Time
Lee, J.; Yi, S. S.; Jeong, M.; Sung, M.; Yoon, W.; Choi, Y.; Ko, M.; and Kang, J. 2020a · 2006
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Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Izacard, G.; and Grave, E. 2020 · 2007
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Instance weighting for domain adaptation in NLP
Jiang, J.; and Zhai, C. 2007 · 2007
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Zhang, E.; Gupta, N.; Tang, R.; Han, X.; Pradeep, R.; Lu, K.; Zhang, Y.; Nogueira, R.; Cho, K.; Fang, H.; et al. 2020 · 2007
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The Probabilistic Relevance Framework: BM25 and Beyond
Robertson, S.; and Zaragoza, H. 2009 · 2009
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Learning Discriminative Projections for Text Similarity Measures
Yih, W.-t.; Toutanova, K.; Platt, J. C.; and Meek, C. 2011 · 2011
Cited alongside, same era.
Semantic parsing on freebase from question-answer pairs
Berant, J.; Chou, A.; Frostig, R.; and Liang, P. 2013 · 2013
Cited alongside, same era.
Modeling of the question answering task in the yodaqa system
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
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Unified Language Model Pre-training for Natural Language Understanding and Generation
Dong, L.; Yang, N.; Wang, W.; Wei, F.; Liu, X.; Wang, Y.; Gao, J.; Zhou, M.; and Hon, H.-W. 2019 · 2019
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Learning Dense Representations for Entity Retrieval
Gillick, D.; Kulkarni, S.; Lansing, L.; Presta, A.; Baldridge, J.; Ie, E.; and Garcia-Olano, D. 2019 · 2019
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Billion-scale similarity search with GPUs
Johnson, J.; Douze, M.; and Jégou, H. 2019 · 2019
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Natural questions: a benchmark for question answering research
Kwiatkowski, T.; Palomaki, J.; Redfield, O.; Collins, M.; Parikh, A.; Alberti, C.; Epstein, D.; Polosukhin, I.; Devlin, J.; Lee, K.; et al. 2019 · 2019
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Baudiš, P.; and Šedivỳ, J. 2015 · 2015
Cited alongside, same era.
Siamese neural networks for one-shot image recognition
Koch, G.; Zemel, R.; and Salakhutdinov, R. 2015 · 2015
Cited alongside, same era.
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P.; Zhang, J.; Lopyrev, K.; and Liang, P. 2016 · 2016
Cited alongside, same era.
Reading Wikipedia to Answer Open-Domain Questions
Chen, D.; Fisch, A.; Weston, J.; and Bordes, A. 2017 · 2017
Cited alongside, same era.
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M.; Choi, E.; Weld, D. S.; and Zettlemoyer, L. 2017 · 2017
Cited alongside, same era.
Neural Domain Adaptation for Biomedical Question Answering
Wiese, G.; Weissenborn, D.; and Neves, M. 2017 · 2017
Cited alongside, same era.
Know What You Don’t Know: Unanswerable Questions for SQuAD
Rajpurkar, P.; Jia, R.; and Liang, P. 2018 · 2018
Cited alongside, same era.
Lee, K.; Chang, M.-W.; and Toutanova, K. 2019 · 2019
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fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Ott, M.; Edunov, S.; Baevski, A.; Fan, A.; Gross, S.; Ng, N.; Grangier, D.; and Auli, M. 2019 · 2019
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Language Models are Unsupervised Multitask Learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; and Sutskever, I. 2019 · 2019
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Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index
Seo, M.; Lee, J.; Kwiatkowski, T.; Parikh, A.; Farhadi, A.; and Hajishirzi, H. 2019 · 2019
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Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks
Gururangan, S.; Marasović, A.; Swayamdipta, S.; Lo, K.; Beltagy, I.; Downey, D.; and Smith, N. A. 2020 · 2020
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COVID-QA: A Question Answering Dataset for COVID-19
Möller, T.; Reina, G. A.; Jayakumar, R.; and Livermore, L. 2020 · 2020
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On the Importance of Diversity in Question Generation for QA
Sultan, M. A.; Chandel, S.; Fernandez Astudillo, R.; and Castelli, V. 2020 · 2020
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Capturing Greater Context for Question Generation
Tuan, L. A.; Shah, D. J.; and Barzilay, R. 2020 · 2020
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CORD-19: The Covid-19 Open Research Dataset
Wang, L. L.; Lo, K.; Chandrasekhar, Y.; Reas, R.; Yang, J.; Eide, D.; Funk, K.; Kinney, R.; Liu, Z.; Merrill, W.; et al. 2020 · 2020
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