Fetching the paper…
Reading the bibliography…
We propose a novel conditioned text generation model.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Design of a knowledge-based report generator
Karen Kukich. 1983 · 1983
Earlier work this paper cites.
Finding structure in time
Jeffrey L. Elman. 1990 · 1990
Earlier work this paper cites.
Text generation
Kathleen McKeown. 1992 · 1992
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Building applied natural language generation systems
Ehud Reiter and Robert Dale. 1997 · 1997
Earlier work this paper cites.
Bidirectional recurrent neural networks
M. Schuster and K.K. Paliwal. 1997 · 1997
Earlier work this paper cites.
Learning to Learn
Sebastian Thrun and Lorien Pratt, editors. 1998 · 1998
Earlier work this paper cites.
Advances in automatic text summarization
Inderjeet Mani. 1999 · 1999
Earlier work this paper cites.
Practical, template–based natural language generation with tag
Tilman Becker. 2002 · 2002
Earlier work this paper cites.
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.
English Gigaword Second Edition
David Graff, Junbo Kong, Ke Chen, and Kazuaki Maeda. 2003 · 2003
Earlier work this paper cites.
Techniques for text planning with xslt
Mary Ellen Foster and Michael White. 2004 · 2004
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Choosing words in computer-generated weather forecasts
Ehud Reiter, Somayajulu Sripada, Jim Hunter, Jin Yu, and Ian Davy. 2005 · 2005
Earlier work this paper cites.
The New York Times Annotated Corpus
Evan Sandaus. 2008 · 2008
Earlier work this paper cites.
SimpleNLG: A realisation engine for practical applications
Albert Gatt and Ehud Reiter. 2009 · 2009
Earlier work this paper cites.
Statistical machine translation
Philipp Koehn. 2009 · 2009
Earlier work this paper cites.
Learning semantic correspondences with less supervision
Percy Liang, Michael I. Jordan, and Dan Klein. 2009 · 2009
Earlier work this paper cites.
A hypercube-based encoding for evolving large-scale neural networks
K. O. Stanley, D. B. D’Ambrosio, and J. Gauci. 2009 · 2009
Earlier work this paper cites.
Evolving neural networks in compressed weight space
Jan Koutnik, Faustino Gomez, and Jürgen Schmidhuber. 2010 · 2010
Earlier work this paper cites.
Supervised Sequence Labelling with Recurrent Neural Networks , volume 385 of Studies in Computational Intelligence
Alex Graves. 2012 · 2012
Earlier work this paper cites.
Annotated gigaword
Courtney Napoles, Matthew Gormley, and Benjamin Van Durme. 2012 · 2012
Cited alongside, same era.
Framing image description as a ranking task: Data, models and evaluation metrics
Micah Hodosh, Peter Young, and Julia Hockenmaier. 2013 · 2013
Cited alongside, same era.
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
Cited alongside, same era.
Domain-specific image captioning
Rebecca Mason and Eugene Charniak. 2014 · 2014
Cited alongside, same era.
GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016 · 2016
Later among the works it cites.
Contextual explanation networks
Maruan Al-Shedivat, Avinava Dubey, and Eric P. Xing. 2017 · 2017
Later among the works it cites.
Quasi-Recurrent Neural Network
James Bradbury, Stephen Merity, Caiming Xiong, and Richard Socher. 2017 · 2017
Later among the works it cites.
Hypernetworks
David Ha, Andrew Dai, and Quoc V Le. 2017 · 2017
Later among the works it cites.
Regularizing rnns by stabilizing activations
David Krueger and Roland Memisevic. 2017 · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015 · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2015 · 2015
Cited alongside, same era.
Variational dropout and the local reparameterization trick
Durk P Kingma, Tim Salimans, and Max Welling. 2015 · 2015
Cited alongside, same era.
Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
ACDC: A structured efficient linear layer
Marcin Moczulski, Misha Denil, Jeremy Appleyard, and Nando de Freitas. 2015 · 2015
Cited alongside, same era.
Later among the works it cites.
Adversarial ranking for language generation
Kevin Lin, Dianqi Li, Xiaodong He, Zhengyou Zhang, and Ming-ting Sun. 2017 · 2017
Later among the works it cites.
Dynamic compositional neural networks over tree structure
Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2017 · 2017
Later among the works it cites.
Deep multitask learning for semantic dependency parsing
Hao Peng, Sam Thomson, and Noah A. Smith. 2017 · 2017
Later among the works it cites.
Using the output embedding to improve language models
Ofir Press and Lior Wolf. 2017 · 2017
Later among the works it cites.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
Later among the works it cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Later among the works it cites.
Challenges in data-to-document generation
Sam Wiseman, Stuart Shieber, and Alexander Rush. 2017 · 2017
Later among the works it cites.
Controllable abstractive summarization
Angela Fan, David Grangier, and Michael Auli. 2018 · 2018
Later among the works it cites.
Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander M Rush. 2018 · 2018
Later among the works it cites.
Search engine guided non-parametric neural machine translation
Jiatao Gu, Yong Wang, Kyunghyun Cho, and Victor OK Li. 2018 · 2018
Later among the works it cites.
Generating sentences by editing prototypes
Kelvin Guu, Tatsunori B Hashimoto, Yonatan Oren, and Percy Liang. 2018 · 2018
Later among the works it cites.
Table-to-text generation by structure-aware seq2seq learning
Tianyu Liu, Kexiang Wang, Lei Sha, Baobao Chang, and Zhifang Sui. 2018 · 2018
Later among the works it cites.
Exemplar encoder-decoder for neural conversation generation
Gaurav Pandey, Danish Contractor, Vineet Kumar, and Sachindra Joshi. 2018 · 2018
Later among the works it cites.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
Later among the works it cites.
Contextual parameter generation for universal neural machine translation
Emmanouil Antonios Platanios, Mrinmaya Sachan, Graham Neubig, and Tom Mitchell. 2018 · 2018
Later among the works it cites.
Retrieve and refine: Improved sequence generation models for dialogue
Jason Weston, Emily Dinan, and Alexander Miller. 2018 · 2018
Later among the works it cites.
Learning neural templates for text generation
Sam Wiseman, Stuart M Shieber, and Alexander M Rush. 2018 · 2018
Later among the works it cites.