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This paper presents BiPaR, a bilingual parallel novel-style machine reading comprehension (MRC) dataset, developed to support multilingual and cross-lingual reading comprehension.
Multi-passage machine reading comprehension with cross-passage answer verification
Yizhong Wang, Kai Liu, Jing Liu, Wei He, Yajuan Lyu, Hua Wu, Sujian Li, and Haifeng Wang. 2018 · 1927
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CMU javelin system for NTCIR5 CLQA1
Frank Lin, Hideki Shima, Mengqiu Wang, and Teruko Mitamura. 2005 · 2005
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Miracle’s 2005 approach to cross-lingual question answering
César de Pablo-Sánchez, Ana González-Ledesma, José Luis Martínez-Fernández, José Maria Guirao, Paloma Martinez, and Antonio Moreno-Sandoval. 2005 · 2005
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Cross-language french-english question answering using the dlt system at clef 2005
Richard FE Sutcliffe, Michael Mulcahy, Igal Gabbay, Aoife O’Gorman, and Darina Slattery. 2005 · 2005
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The multilingual question answering track at CLEF
Bernardo Magnini, Danilo Giampiccolo, Lili Aunimo, Christelle Ayache, Petya Osenova, Anselmo Peñas, Maarten de Rijke, Bogdan Sacaleanu, Diana Santos, and Richard Sutcliffe. 2006 · 2006
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Enhancing cross-language question answering by combining multiple question translations
Rita Marina Aceves-Pérez, Manuel Montes-y Gómez, and Luis Villaseñor-Pineda. 2007 · 2007
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Two approaches for multilingual question answering: Merging passages vs. merging answers
Rita M Aceves-Pérez, Manuel Montes-y Gómez, Luis Villaseñor-Pineda, and L Alfonso Ureña-López. 2008 · 2008
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Overview of respubliqa 2009: question answering evaluation over european legislation
Anselmo Peñas, Pamela Forner, Richard Sutcliffe, Álvaro Rodrigo, Corina Forăscu, Iñaki Alegria, Danilo Giampiccolo, Nicolas Moreau, and Petya Osenova. 2009 · 2009
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Information retrieval baselines for the respubliqa task
Joaquín Pérez, Guillermo Garrido, A Rodrigo, Lourdes Araujo, and Anselmo Peñas. 2009 · 2009
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Multi-task learning for multiple language translation
Daxiang Dong, Hua Wu, Wei He, Dianhai Yu, and Haifeng Wang. 2015 · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomáš Kočiskỳ, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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A thorough examination of the cnn/daily mail reading comprehension task
Danqi Chen, Jason Bolton, and Christopher D Manning. 2016 · 2016
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Consensus attention-based neural networks for chinese reading comprehension
Yiming Cui, Ting Liu, Zhipeng Chen, Shijin Wang, and Guoping Hu. 2016 · 2016
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Text understanding with the attention sum reader network
Rudolf Kadlec, Martin Schmid, Ondřej Bajgar, and Jan Kleindienst. 2016 · 2016
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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
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
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Multilingual extractive reading comprehension by runtime machine translation
Akari Asai, Akiko Eriguchi, Kazuma Hashimoto, and Yoshimasa Tsuruoka. 2018 · 2018
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Neural Reading Comprehension and Beyond
Danqi Chen. 2018 · 2018
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A span-extraction dataset for chinese machine reading comprehension
Yiming Cui, Ting Liu, Li Xiao, Zhipeng Chen, Wentao Ma, Wanxiang Che, Shijin Wang, and Guoping Hu. 2018 · 2018
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DuReader: a chinese machine reading comprehension dataset from real-world applications
Wei He, Kai Liu, Jing Liu, Yajuan Lyu, Shiqi Zhao, Xinyan Xiao, Yuan Liu, Yizhong Wang, Hua Wu, Qiaoqiao She, et al. 2018 · 2018
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The narrativeqa reading comprehension challenge
Tomas Kocisky, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gabor Melis, and Edward Grefenstette. 2018 · 2018
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Caiming Xiong, Victor Zhong, and Richard Socher. 2016 · 2016
Cited alongside, same era.
Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
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Attention-over-attention neural networks for reading comprehension
Yiming Cui, Zhipeng Chen, Si Wei, Shijin Wang, Ting Liu, and Guoping Hu. 2017 · 2017
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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, et al. 2017 · 2017
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
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Race: Large-scale reading comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017
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Reasonet: Learning to stop reading in machine comprehension
Yelong Shen, Po-Sen Huang, Jianfeng Gao, and Weizhu Chen. 2017 · 2017
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Analogical reasoning on chinese morphological and semantic relations
Shen Li, Zhe Zhao, Renfen Hu, Wensi Li, Tao Liu, and Xiaoyong Du. 2018 · 2018
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Stochastic answer networks for machine reading comprehension
Xiaodong Liu, Yelong Shen, Kevin Duh, and Jianfeng Gao. 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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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
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Qanet: Combining local convolution with global self-attention for reading comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V Le. 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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CoQA: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D Manning. 2019 · 2019
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