Fetching the paper…
Reading the bibliography…
We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models.
Conceptual processing of text during skimming and rapid sequential reading
Michael EJ Masson. 1983 · 1983
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
Earlier work this paper cites.
Building a question answering test collection
Ellen M Voorhees and Dawn M Tice. 2000 · 2000
Earlier work this paper cites.
Multi-level structured models for document-level sentiment classification
Ainur Yessenalina, Yisong Yue, and Claire Cardie. 2010 · 2010
Earlier work this paper cites.
Natural language processing (almost) from scratch
R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa. 2011 · 2011
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey J Gordon, and Drew Bagnell. 2011 · 2011
Earlier work this paper cites.
Mctest: A challenge dataset for the open-domain machine comprehension of text
Matthew Richardson, Christopher JC Burges, and Erin Renshaw. 2013 · 2013
Earlier work this paper cites.
Multiple object recognition with visual attention
Jimmy Ba, Volodymyr Mnih, and Koray Kavukcuoglu. 2014 · 2014
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
A convolutional neural network for modelling sentences
Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. 2014 · 2014
Earlier work this paper cites.
Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
Earlier work this paper cites.
Deep Learning for Answer Sentence Selection
Lei Yu, Karl Moritz Hermann, Phil Blunsom, and Stephen Pulman. 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
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
The goldilocks principle: Reading children’s books with explicit memory representations
Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2015 · 2015
Cited alongside, same era.
Language understanding for text-based games using deep reinforcement learning
Karthik Narasimhan, Tejas Kulkarni, and Regina Barzilay. 2015 · 2015
Cited alongside, same era.
Learning to rank short text pairs with convolutional deep neural networks
Aliaksei Severyn and Alessandro Moschitti. 2015 · 2015
Cited alongside, same era.
A long short-term memory model for answer sentence selection in question answering
Di Wang and Eric Nyberg. 2015 · 2015
Cited alongside, same era.
Towards ai-complete question answering: A set of prerequisite toy tasks
Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M Rush, Bart van Merriënboer, Armand Joulin, and Tomas Mikolov. 2015 · 2015
Cited alongside, same era.
Text understanding with the attention sum reader network
Rudolf Kadlec, Martin Schmid, Ondřej Bajgar, and Jan Kleindienst. 2016 · 2016
Closest in time.
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 · 2016
Closest in time.
Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi S. Jaakkola. 2016 · 2016
Closest in time.
Key-value memory networks for directly reading documents
Alexander Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, and Jason Weston. 2016 · 2016
Closest in time.
Improving information extraction by acquiring external evidence with reinforcement learning
Karthik Narasimhan, Adam Yala, and Regina Barzilay. 2016 · 2016
Closest in time.
MS MARCO: A human generated machine reading comprehension dataset
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, and Yoshua Bengio. 2015 · 2015
Cited alongside, same era.
Learning to compose neural networks for question answering
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. 2016 · 2016
Cited alongside, same era.
A thorough examination of the cnn/daily mail reading comprehension task
Danqi Chen, Jason Bolton, and Christopher D. Manning. 2016 · 2016
Cited alongside, same era.
Neural summarization by extracting sentences and words
Jianpeng Cheng and Mirella Lapata. 2016 · 2016
Cited alongside, same era.
Deep reinforcement learning for mention-ranking coreference models
Kevin Clark and Christopher D. Manning. 2016 · 2016
Cited alongside, same era.
Cícero Nogueira dos Santos, Ming Tan, Bing Xiang, and Bowen Zhou. 2016 · 2016
Cited alongside, same era.
Deep reinforcement learning with an unbounded action space
Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, and Mari Ostendorf. 2016 · 2016
Cited alongside, same era.
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
Closest in time.
Who did what: A large-scale person-centered cloze dataset
Takeshi Onishi, Hai Wang, Mohit Bansal, Kevin Gimpel, and David McAllester. 2016 · 2016
Closest in time.
Squad: 100,000+ questions for machine comprehension of text
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang. 2016 · 2016
Closest in time.
Easy questions first? a case study on curriculum learning for question answering
Mrinmaya Sachan and Eric P Xing. 2016 · 2016
Closest in time.
Query-reduction networks for question answering
Minjoon Seo, Sewon Min, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
Closest in time.
A joint model for answer sentence ranking and answer extraction
Md. Arafat Sultan, Vittorio Castelli, and Radu Florian. 2016 · 2016
Closest in time.
Multi-perspective context matching for machine comprehension
Zhiguo Wang, Haitao Mi, Wael Hamza, and Radu Florian. 2016 · 2016
Closest in time.
Dynamic coattention networks for question answering
Caiming Xiong, Victor Zhong, and Richard Socher. 2016 · 2016
Closest in time.