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Deep learning has improved performance on many natural language processing (NLP) tasks individually.
Understanding natural language
T. Winograd · 1972
Earlier work this paper cites.
The need for biases in learning generalizations
T. M. Mitchell · 1980
Earlier work this paper cites.
Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
J. Schmidhuber · 1987
Earlier work this paper cites.
Catastrophic interference in connectionist networks: The sequential learning problem
M. McCloskey and N. J. Cohen · 1989
Earlier work this paper cites.
Connectionist models of recognition memory: constraints imposed by learning and forgetting functions
R. M. Ratcliff · 1990
Earlier work this paper cites.
On the optimization of a synaptic learning rule
S. Bengio, Y. Bengio, J. Cloutier, and J. Gecsei · 1992
Earlier work this paper cites.
Learning to control fast-weight memories: An alternative to dynamic recurrent networks
J. Schmidhuber · 1992
Earlier work this paper cites.
Catastrophic forgetting, rehearsal and pseudorehearsal
A. Robins · 1995
Earlier work this paper cites.
Multitask learning
R. Caruana · 1997
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Lifelong learning algorithms
S. Thrun · 1998
Earlier work this paper cites.
Learning to learn: Introduction and overview
S. Thrun and L. Pratt · 1998
Earlier work this paper cites.
A natural logic inference system
Y. Fyodorov, Y. Winter, and N. Francez · 2000
Earlier work this paper cites.
Learning to learn using gradient descent
S. Hochreiter, A. S. Younger, and P. R. Conwell · 2001
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu · 2002
Earlier work this paper cites.
A perspective view and survey of meta-learning
R. Vilalta and Y. Drissi · 2002
Earlier work this paper cites.
Entailment, intensionality and text understanding
C. Condoravdi, D. Crouch, V. de Paiva, R. Stolle, and D. G. Bobrow · 2003
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
C.-Y. Lin · 2004
Earlier work this paper cites.
Recognising textual entailment with robust logical inference
J. Bos and K. Markert · 2005
Earlier work this paper cites.
The pascal recognising textual entailment challenge
I. Dagan, O. Glickman, and B. Magnini · 2005
Earlier work this paper cites.
Framewise phoneme classification with bidirectional lstm and other neural network architectures
A. Graves and J. Schmidhuber · 2005
Earlier work this paper cites.
Ontonotes: The 90
E. H. Hovy, M. P. Marcus, M. Palmer, L. A. Ramshaw, and R. M. Weischedel · 2006
Earlier work this paper cites.
A unified architecture for natural language processing: deep neural networks with multitask learning
R. Collobert and J. Weston · 2008
Earlier work this paper cites.
Dependency-based semantic role labeling of propbank
R. Johansson and P. Nugues · 2008
Earlier work this paper cites.
The importance of syntactic parsing and inference in semantic role labeling
V. Punyakanok, D. Roth, and W. tau Yih · 2008
Earlier work this paper cites.
Curriculum learning
Y. Bengio, J. Louradour, R. Collobert, and J. Weston · 2009
Earlier work this paper cites.
An extended model of natural logic
B. MacCartney and C. D. Manning · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
Earlier work this paper cites.
Natural language processing (almost) from scratch
R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. P. Kuksa · 2011
Earlier work this paper cites.
The winograd schema challenge
H. J. Levesque, E. Davis, and L. Morgenstern · 2011
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. Manning, A. Ng, and C. Potts · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2014
Earlier work this paper cites.
Multi-objective optimization
K. Deb · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
J. Pennington, R. Socher, and C. D. Manning · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning · 2015
Earlier work this paper cites.
Question-answer driven semantic role labeling: Using natural language to annotate natural language
L. He, M. Lewis, and L. S. Zettlemoyer · 2015
Earlier work this paper cites.
Teaching machines to read and comprehend
K. M. Hermann, T. Kociský, E. Grefenstette, L. Espeholt, W. Kay, M. Suleyman, and P. Blunsom · 2015
Earlier work this paper cites.
Skip-thought vectors
R. Kiros, Y. Zhu, R. Salakhutdinov, R. S. Zemel, R. Urtasun, A. Torralba, and S. Fidler · 2015
Cited alongside, same era.
From group to individual labels using deep features
D. Kotzias, M. Denil, N. De Freitas, and P. Smyth · 2015
Cited alongside, same era.
Improved semantic representations from tree-structured long short-term memory networks
K. S. Tai, R. Socher, and C. D. Manning · 2015
Cited alongside, same era.
Pointer networks
O. Vinyals, M. Fortunato, and N. Jaitly · 2015
Cited alongside, same era.
End-to-end learning of semantic role labeling using recurrent neural networks
J. Zhou and W. Xu · 2015
Cited alongside, same era.
Learning to learn by gradient descent by gradient descent
M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau, T. Schaul, and N. de Freitas · 2016
Cited alongside, same era.
A simple and accurate syntax-agnostic neural model for dependency-based semantic role labeling
D. Marcheggiani, A. Frolov, and I. Titov · 2017
Later among the works it cites.
Learned in translation: Contextualized word vectors
B. McCann, J. Bradbury, C. Xiong, and R. Socher · 2017
Later among the works it cites.
Pointer sentinel mixture models
S. Merity, C. Xiong, J. Bradbury, and R. Socher · 2017
Later among the works it cites.
Question answering through transfer learning from large fine-grained supervision data
S. Min, M. J. Seo, and H. Hajishirzi · 2017
Later among the works it cites.
Memen: Multi-layer embedding with memory networks for machine comprehension
B. Pan, H. Li, Z. Zhao, B. Cao, D. Cai, and X. He · 2017
Later among the works it cites.
Towards improving abstractive summarization via entailment generation
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J. Ba, R. Kiros, and G. E. Hinton · 2016
Cited alongside, same era.
The iwslt 2016 evaluation campaign
M. Cettolo, J. Niehues, S. Stüker, L. Bentivogli, R. Cattoni, and M. J. Federico · 2016
Cited alongside, same era.
A bio-inspired incremental learning architecture for applied perceptual problems
A. Gepperth and C. Karaoguz · 2016
Cited alongside, same era.
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
J. Gu, Z. Lu, H. Li, and V. O. K. Li · 2016
Cited alongside, same era.
c. Gülçehre, S. Ahn, R. Nallapati, B. Zhou, and Y. Bengio · 2016
Cited alongside, same era.
A joint many-task model: Growing a neural network for multiple NLP tasks
K. Hashimoto, C. Xiong, Y. Tsuruoka, and R. Socher · 2016
Cited alongside, same era.
R. Pasunuru, H. Guo, and M. Bansal · 2017
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A deep reinforced model for abstractive summarization
R. Paulus, C. Xiong, and R. Socher · 2017
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Learning to generate reviews and discovering sentiment
A. Radford, R. Józefowicz, and I. Sutskever · 2017
Later among the works it cites.
Unsupervised pretraining for sequence to sequence learning
P. Ramachandran, P. J. Liu, and Q. V. Le · 2017
Later among the works it cites.
Sluice networks: Learning what to share between loosely related tasks
S. Ruder, J. Bingel, I. Augenstein, and A. Sogaard · 2017
Later among the works it cites.
Contextualized word representations for reading comprehension
S. Salant and J. Berant · 2017
Later among the works it cites.
Get to the point: Summarization with pointer-generator networks
A. See, P. J. Liu, and C. D. Manning · 2017
Later among the works it cites.
The university of edinburgh’s neural mt systems for wmt17
R. Sennrich, A. Birch, A. Currey, U. Germann, B. Haddow, K. Heafield, A. V. M. Barone, and P. Williams · 2017
Later among the works it cites.
Bidirectional attention flow for machine comprehension
M. Seo, A. Kembhavi, A. Farhadi, and H. Hajishirzi · 2017
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Cutting-off redundant repeating generations for neural abstractive summarization
J. Suzuki and M. Nagata · 2017
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Deep semantic role labeling with self-attention
Z. Tan, M. Wang, J. Xie, Y. Chen, and X. Shi · 2017
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A compare-propagate architecture with alignment factorization for natural language inference
Y. Tay, L. A. Tuan, and S. C. Hui · 2017
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Newsqa: A machine comprehension dataset
A. Trischler, T. Wang, X. Yuan, J. Harris, A. Sordoni, P. Bachman, and K. Suleman · 2017
Later among the works it cites.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
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Machine comprehension using Match-LSTM and answer pointer
S. Wang and J. Jiang · 2017
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Making neural qa as simple as possible but not simpler
D. Weissenborn, G. Wiese, and L. Seiffe · 2017
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A broad-coverage challenge corpus for sentence understanding through inference
A. Williams, N. Nangia, and S. R. Bowman · 2017
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Dynamic coattention networks for question answering
C. Xiong, V. Zhong, and R. Socher · 2017
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Seq2sql: Generating structured queries from natural language using reinforcement learning
V. Zhong, C. Xiong, and R. Socher · 2017
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The best of both worlds: Combining recent advances in neural machine translation
M. X. Chen, O. Firat, A. Bapna, M. Johnson, W. Macherey, G. Foster, L. Jones, N. Parmar, M. Schuster, Z. Chen, et al · 2018
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Dr-bilstm: Dependent reading bidirectional lstm for natural language inference
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Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies
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Fine-tuned language models for text classification
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Reinforced mnemonic reader for machine reading comprehension
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Natural language to structured query generation via meta-learning
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Semantic sentence matching with densely-connected recurrent and co-attentive information
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Reinforced self-attention network: a hybrid of hard and soft attention for sequence modeling
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