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Recently, reading comprehension models achieved near-human performance on large-scale datasets such as SQuAD, CoQA, MS Macro, RACE, etc.
Cross-lingual language model pretraining
Guillaume Lample and Alexis Conneau. 2019 · 1901
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Rodrigo Nogueira and Kyunghyun Cho. 2019 · 1901
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Improving question answering with external knowledge
Xiaoman Pan, Kai Sun, Dian Yu, Heng Ji, and Dong Yu. 2018 · 1902
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XLNet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. 2019b · 1906
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A teacher-student framework for zero-resource neural machine translation
Yun Chen, Yang Liu, Yong Cheng, and Victor O.K. Li. 2017b · 1935
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Building an inflectional stemmer for Bulgarian
Preslav Nakov. 2003 · 2003
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Searching strategies for the Bulgarian language
Jacques Savoy. 2007 · 2007
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The probabilistic relevance framework: BM25 and beyond
Stephen Robertson and Hugo Zaragoza. 2009 · 2009
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Overview of QA4MRE at CLEF 2012: Question answering for machine reading evaluation
Anselmo Peñas, Eduard Hovy, Pamela Forner, Álvaro Rodrigo, Richard Sutcliffe, Corina Forascu, Yassine Benajiba, and Petya Osenova. 2012 · 2012
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Bulgarian question answering for machine reading
Kiril Ivanov Simov, Petya Osenova, Georgi Georgiev, Valentin Zhikov, and Laura Tolosi. 2012 · 2012
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MCTest: A challenge dataset for the open-domain machine comprehension of text
Matthew Richardson, Christopher J.C. Burges, and Erin Renshaw. 2013 · 2013
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Overview of CLEF question answering track 2014
Anselmo Peñas, Christina Unger, and Axel-Cyrille Ngonga Ngomo. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Combining retrieval, statistics, and inference to answer elementary science questions
Peter Clark, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Turney, and Daniel Khashabi. 2016 · 2016
Cited alongside, same era.
Zero-resource translation with multi-lingual neural machine translation
Orhan Firat, Baskaran Sankaran, Yaser Al-Onaizan, Fatos T. Yarman Vural, and Kyunghyun Cho. 2016 · 2016
Cited alongside, same era.
MS MARCO: A human generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
Cited alongside, same era.
Google’s multilingual neural machine translation system: Enabling zero-shot translation
Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2017 · 2017
Cited alongside, same era.
TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
Cited alongside, same era.
Learning word vectors for 157 languages
Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, and Tomas Mikolov. 2018 · 2018
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Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
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Can a suit of armor conduct electricity? A new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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Cross-language learning with adversarial neural networks
Shafiq Joty, Preslav Nakov, Lluís Màrquez, and Israa Jaradat. 2017 · 2017
Cited alongside, same era.
RACE: Large-scale ReAding comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017
Cited alongside, same era.
NewsQA: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond
Mikel Artetxe and Holger Schwenk. 2018 · 2018
Cited alongside, same era.
Multilingual extractive reading comprehension by runtime machine translation
Akari Asai, Akiko Eriguchi, Kazuma Hashimoto, and Yoshimasa Tsuruoka. 2018 · 2018
Cited alongside, same era.
Think you have solved question answering? Try ARC, the AI2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
Multi-range reasoning for machine comprehension
Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2018 · 2018
Later among the works it cites.
Massively multilingual neural machine translation
Roee Aharoni, Melvin Johnson, and Orhan Firat. 2019 · 2019
Closest in time.
Transformer-XL: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, and Ruslan Salakhutdinov. 2019 · 2019
Closest in time.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Closest in time.
Learning to attend on essential terms: An enhanced retriever-reader model for open-domain question answering
Jianmo Ni, Chenguang Zhu, Weizhu Chen, and Julian McAuley. 2019 · 2019
Closest in time.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Closest in time.
CoQA: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D. Manning. 2019 · 2019
Closest in time.