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End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document.
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Hoa Trang Dang · 2006
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Surabhi Gupta, Ani Nenkova, and Dan Jurafsky · 2007
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Slav Orlinov Petrov · 2009
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Wang Lu, Hema Raghavan, Vittorio Castelli, Radu Florian, and Claire Cardie · 2013
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MCTest: A challenge dataset for the open-domain
Matthew Richardson, Christopher J. C. Burges, and Erin Renshaw · 2013
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A neural network for factoid question answering over paragraphs
Mohit Iyyer, Jordan Boyd-Graber, Leonardo Claudino, Richard Socher, and Hal Daumé III · 2014
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Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Rose Finkel, Steven Bethard, and David McClosky · 2014
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Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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A thorough examination of the CNN/Daily Mail reading comprehension task
Danqi Chen, Jason Bolton, and Christopher D. Manning · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
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Diederik P. Kingma and Jimmy Ba · 2015
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A neural attention model for abstractive sentence summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston · 2015
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End-to-end memory networks
Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus · 2015
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Memory networks
Jason Weston, Sumit Chopra, and Antoine Bordes · 2015
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WikiQA: A challenge dataset for open-domain question answering
Yi Yang, Wen tau Yih, and Christopher Meek · 2015
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WIKIREADING: A novel large-scale language understanding task over Wikipedia
Daniel Hewlett, Alexandre Lacoste, Llion Jones, Illia Polosukhin, Andrew Fandrianto, Jay Han, Matthew Kelcey, and David Berthelot · 2016
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Ask me anything: Dynamic memory networks for natural language processing
Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, and Richard Socher · 2016
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Hierarchical question-image co-attention for visual question answering
Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh · 2016
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SQuAD: 100, 000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Question answering by reasoning across documents with graph convolutional networks
Nicola De Cao, Wilker Aziz, and Ivan Titov · 2018
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Simple and effective multi-paragraph reading comprehension
Christopher Clark and Matt Gardner · 2018
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Dynamic self-attention: Computing attention over words dynamically for sentence embedding
SangKeun Lee Deunsol Yoon, Dongbok Lee · 2018
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Neural models for reasoning over multiple mentions using coreference
Bhuwan Dhingra, Qiao Jin, Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov · 2018
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Coarse-to-fine decoding for neural semantic parsing
Li Dong and Mirella Lapata · 2018
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Eunsol Choi, Daniel Hewlett, Jakob Uszkoreit, Illia Polosukhin, Alexandre Lacoste, and Jonathan Berant · 2017
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A joint many-task model: Growing a neural network for multiple NLP tasks
Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, and Richard Socher · 2017
Cited alongside, same era.
TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer · 2017
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End-to-end neural coreference resolution
Kenton Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer · 2017
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SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Learned in translation: Contextualized word vectors
Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher · 2017
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Bidirectional attention flow for machine comprehension
Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi · 2017
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Constituency parsing with a self-attentive encoder
Nikita Kitaev and Dan Klein · 2018
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Efficient and robust question answering from minimal context over documents
Sewon Min, Victor Zhong, Richard Socher, and Caiming Xiong · 2018
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang · 2018
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Reinforced self-attention network: a hybrid of hard and soft attention for sequence modeling
Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Sen Wang, and Chengqi Zhang · 2018
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Linfeng Song, Zhiguo Wang, Mo Yu, Yue Zhang, Radu Florian, and Daniel Gildea · 2018
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Multi-mention learning for reading comprehension with neural cascades
Swabha Swayamdipta, Ankur P. Parikh, and Tom Kwiatkowski · 2018
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Evidence aggregation for answer re-ranking in open-domain question answering
Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, and Zhiguo Wang · 2018
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Constructing datasets for multi-hop reading comprehension across documents
Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel · 2018
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DCN+: Mixed objective and deep residual coattention for question answering
Caiming Xiong, Victor Zhong, and Richard Socher · 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
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Global-locally self-attentive dialogue state tracker
Victor Zhong, Caiming Xiong, and Richard Socher · 2018
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