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Despite recent work in Reading Comprehension (RC), progress has been mostly limited to English due to the lack of large-scale datasets in other languages.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Bruja: Question classification for spanish. using machine translationand an english classifier
Miguel Á. García Cumbreras, L. Alfonso Ureña López, and Fernando Martínez Santiago. 2006 · 2006
Earlier work this paper cites.
Equer: the french evaluation campaign of question-answering systems
Christelle Ayache, Brigitte Grau, and Anne Vilnat. 2006 · 2006
Earlier work this paper cites.
Keyword translation accuracy and cross-lingual question answering inchinese and japanese
Teruko Mitamura, Mengqiu Wang, Hideki Shima, and Frank Lin. 2006 · 2006
Earlier work this paper cites.
Parallel corpora for medium density languages
Dániel Varga, Péter Halácsy, András Kornai, Viktor Nagy, László Németh, and Viktor Trón. 2007 · 2007
Earlier work this paper cites.
Efficient Question Answering with Question Decomposition and Multiple Answer Streams
Sven Hartrumpf, Ingo Glöckner, and Johannes Leveling. 2009 · 2009
Earlier work this paper cites.
Comparative experiments for multilingual sentiment analysis using machine translation
Alexandra Balahur and Marco Turchi. 2012 · 2012
Earlier work this paper cites.
Ualacant: Using online machine translation for cross-lingual textual entailment
Miquel Esplà-Gomis, Felipe Sánchez-Martínez, and Mikel L. Forcada. 2012 · 2012
Earlier work this paper cites.
On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013 · 2013
Earlier work this paper cites.
On the properties of neural machine translation: Encoder–decoder approaches
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 2014
Earlier work this paper cites.
Treebank translation for cross-lingual parser induction
Jörg Tiedemann, Željko Agić, and Joakim Nivre. 2014 · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Cited alongside, same era.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Cited alongside, same era.
Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
Aspec: Asian scientific paper excerpt corpus
Toshiaki Nakazawa, Manabu Yaguchi, Kiyotaka Uchimoto, Masao Utiyama, Eiichiro Sumita, Sadao Kurohashi, and Hitoshi Isahara. 2016 · 2016
Cited alongside, same era.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Cited alongside, same era.
Modeling coverage for neural machine translation
Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li. 2016 · 2016
Google’s multilingual neural machine translation system: Enabling zero-shot translation
Melvin Johnson, Mike Schuster, Quoc Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernand a Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2017 · 2017
Later among the works it cites.
TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
Later among the works it cites.
A Simple and Strong Baseline: NAIST-NICT Neural Machine Translation System for WAT2017 English-Japanese Translation Task
Yusuke Oda, Katsuhito Sudoh, Satoshi Nakamura, Masao Utiyama, and Eiichiro Sumita. 2017 · 2017
Later among the works it cites.
Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
Later among the works it cites.
Gated self-matching networks for reading comprehension and question answering
Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, and Ming Zhou. 2017 · 2017
Later among the works it cites.
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Cited alongside, same era.
Learning to translate for multilingual question answering
Ferhan Ture and Elizabeth Boschee. 2016 · 2016
Cited alongside, same era.
Transfer learning for low-resource neural machine translation
Barret Zoph, Deniz Yuret, Jonathan May, and Kevin Knight. 2016 · 2016
Cited alongside, same era.
An empirical comparison of domain adaptation methods for neural machine translation
Chenhui Chu, Raj Dabre, and Sadao Kurohashi. 2017 · 2017
Cited alongside, same era.
Simple and effective multi-paragraph reading comprehension
Christopher Clark and Matt Gardner. 2017 · 2017
Cited alongside, same era.
Detecting untranslated content for neural machine translation
Isao Goto and Hideki Tanaka. 2017 · 2017
Cited alongside, same era.
Dureader: a chinese machine reading comprehension dataset from real-world applications
Wei He, Kai Liu, Yajuan Lyu, Shiqi Zhao, Xinyan Xiao, Yuan Liu, Yizhong Wang, Hua Wu, Qiaoqiao She, Xuan Liu, et al. 2017 · 2017
Cited alongside, same era.
Making neural qa as simple as possible but not simpler
Dirk Weissenborn, Georg Wiese, and Laura Seiffe. 2017 · 2017
Later among the works it cites.
Exploring question understanding and adaptation in neural-network-based question answering
Junbei Zhang, Xiaodan Zhu, Qian Chen, Lirong Dai, and Hui Jiang. 2017 · 2017
Later among the works it cites.
Reinforced mnemonic reader for machine reading comprehension
Minghao Hu, Yuxing Peng, and Xipeng Qiu. 2018 · 2018
Closest in time.
Semi-supervised training data generation for multilingual question answering
Kyungjae Lee, Kyoungho Yoon, Sunghyun Park, and Seung-won Hwang. 2018 · 2018
Closest in time.
Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Closest in time.
DCN+: Mixed objective and deep residual coattention for question answering
Caiming Xiong, Victor Zhong, and Richard Socher. 2018 · 2018
Closest in time.
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
Closest in time.