Fetching the paper…
Reading the bibliography…
Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear.
Brown Corpus Manual
W. N. Francis and H. Kucera. 1979 · 1979
Earlier work this paper cites.
WordNet: An Electronic Lexical Database
C. Fellbaum. 1998 · 1998
Earlier work this paper cites.
Treebank-3
M. Marcus, B. Santorini, M. A. Marcinkiewicz, and A. Taylor. 1999 · 1999
Earlier work this paper cites.
Adversarial classification
N. Dalvi, P. Domingos, Mausam, S. Sanghai, and D. Verma. 2004 · 2004
Earlier work this paper cites.
Adversarial learning
D. Lowd and C. Meek. 2005 · 2005
Earlier work this paper cites.
Nightmare at test time: robust learning by feature deletion
A. Globerson and S. Roweis. 2006 · 2006
Earlier work this paper cites.
Unbounded dependency recovery for parser evaluation
L. Rimell, S. Clark, and M. Steedman. 2009 · 2009
Earlier work this paper cites.
Generating phrasal and sentential paraphrases: A survey of data-driven methods
N. Madnani and B. J. Dorr. 2010 · 2010
Earlier work this paper cites.
Adversarial evaluation for models of natural language
N. A. Smith. 2012 · 2012
Earlier work this paper cites.
On our best behaviour
H. J. Levesque. 2013 · 2013
Earlier work this paper cites.
Generative adversarial nets
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014 · 2014
Earlier work this paper cites.
The stanford coreNLP natural language processing toolkit
C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard, and D. McClosky. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
J. Pennington, R. Socher, and C. D. Manning. 2014 · 2014
Cited alongside, same era.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2014 · 2014
Cited alongside, same era.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy. 2015 · 2015
Cited alongside, same era.
Generating sentences from a continuous space
S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio. 2016 · 2016
Cited alongside, same era.
Data recombination for neural semantic parsing
R. Jia and P. Liang. 2016 · 2016
Cited alongside, same era.
Simple black-box adversarial perturbations for deep networks
N. Narodytska and S. P. Kasiviswanathan. 2016 · 2016
Build it, break it: The language edition
E. M. Bender, H. Daume III, A. Ettinger, H. Kannan, S. Rao, and E. Rothschild. 2017 · 2017
Closest in time.
Ruminating reader: Reasoning with gated multi-hop attention
Y. Gong and S. R. Bowman. 2017 · 2017
Closest in time.
Mnemonic reader for machine comprehension
M. Hu, Y. Peng, and X. Qiu. 2017 · 2017
Closest in time.
Learning recurrent span representations for extractive question answering
K. Lee, S. Salant, T. Kwiatkowski, A. Parikh, D. Das, and J. Berant. 2017 · 2017
Closest in time.
Adversarial learning for neural dialogue generation
J. Li, W. Monroe, T. Shi, A. Ritter, and D. Jurafsky. 2017 · 2017
Closest in time.
Structural embedding of syntactic trees for machine comprehension
R. Liu, J. Hu, W. Wei, Z. Yang, and E. Nyberg. 2017 · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
The LAMBADA dataset: Word prediction requiring a broad discourse context
D. Paperno, G. Kruszewski, A. Lazaridou, Q. N. Pham, R. Bernardi, S. Pezzelle, M. Baroni, G. Boleda, and R. Fernandez. 2016 · 2016
Cited alongside, same era.
Squad: 100,000+ questions for machine comprehension of text
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang. 2016 · 2016
Cited alongside, same era.
Bidirectional attention flow for machine comprehension
M. Seo, A. Kembhavi, A. Farhadi, and H. Hajishirzi. 2016 · 2016
Cited alongside, same era.
Machine comprehension using match-LSTM and answer pointer
S. Wang and J. Jiang. 2016 · 2016
Cited alongside, same era.
Multi-perspective context matching for machine comprehension
Z. Wang, H. Mi, W. Hamza, and R. Florian. 2016 · 2016
Cited alongside, same era.
End-to-end answer chunk extraction and ranking for reading comprehension
Y. Yu, W. Zhang, K. Hasan, M. Yu, B. Xiang, and B. Zhou. 2016 · 2016
Cited alongside, same era.
Closest in time.
Universal adversarial perturbations
S. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard. 2017 · 2017
Closest in time.
Practical black-box attacks against deep learning systems using adversarial examples
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. Celik, and A. Swami. 2017 · 2017
Closest in time.
Reasonet: Learning to stop reading in machine comprehension
Y. Shen, P. Huang, J. Gao, and W. Chen. 2017 · 2017
Closest in time.
Making neural qa as simple as possible but not simpler
D. Weissenborn, G. Wiese, and L. Seiffe. 2017 · 2017
Closest in time.
Exploring question understanding and adaptation in neural-network-based question answering
J. Zhang, X. Zhu, Q. Chen, L. Dai, S. Wei, and H. Jiang. 2017 · 2017
Closest in time.