Fetching the paper…
Reading the bibliography…
While neural, encoder-decoder models have had significant empirical success in text generation, there remain several unaddressed problems with this style of generation.
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.
A tutorial on hidden markov models and selected applications in speech recognition
Lawrence R Rabiner. 1989 · 1989
Earlier work this paper cites.
Text generation - using discourse strategies and focus constraints to generate natural language text
Kathleen McKeown. 1992 · 1992
Earlier work this paper cites.
The theory of segmental hidden Markov models
Mark JF Gales and Steve J Young. 1993 · 1993
Earlier work this paper cites.
From hmm’s to segment models: A unified view of stochastic modeling for speech recognition
Mari Ostendorf, Vassilios V Digalakis, and Owen A Kimball. 1996 · 1996
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.
Yag: A template-based generator for real-time systems
Susan W McRoy, Songsak Channarukul, and Syed S Ali. 2000 · 2000
Earlier work this paper cites.
Hidden semi-markov models (hsmms)
Kevin P Murphy. 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.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie. 2005 · 2005
Earlier work this paper cites.
Comparing automatic and human evaluation of nlg systems
Anja Belz and Ehud Reiter. 2006 · 2006
Earlier work this paper cites.
Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models
Anja Belz. 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.
Learning semantic correspondences with less supervision
Percy Liang, Michael I Jordan, and Dan Klein. 2009 · 2009
Earlier work this paper cites.
A simple domain-independent probabilistic approach to generation
Gabor Angeli, Percy Liang, and Dan Klein. 2010 · 2010
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton. 2010 · 2010
Cited alongside, same era.
Domain adaptable semantic clustering in statistical nlg
Blake Howald, Ravikumar Kondadadi, and Frank Schilder. 2013 · 2013
Cited alongside, same era.
A statistical nlg framework for aggregated planning and realization
Ravi Kondadadi, Blake Howald, and Frank Schilder. 2013 · 2013
Cited alongside, same era.
A global model for concept-to-text generation
Ioannis Konstas and Mirella Lapata. 2013 · 2013
Cited alongside, same era.
Domain-independent abstract generation for focused meeting summarization
Lu Wang and Claire Cardie. 2013 · 2013
Cited alongside, same era.
On the properties of neural machine translation: Encoder-decoder approaches
KyungHyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014 · 2014
End-to-end training approaches for discriminative segmental models
Hao Tang, Weiran Wang, Kevin Gimpel, and Karen Livescu. 2016 · 2016
Later among the works it cites.
Unsupervised neural hidden markov models
Ke M Tran, Yonatan Bisk, Ashish Vaswani, Daniel Marcu, and Kevin Knight. 2016 · 2016
Later among the works it cites.
Reference-aware language models
Zichao Yang, Phil Blunsom, Chris Dyer, and Wang Ling. 2016 · 2016
Later among the works it cites.
Online segment to segment neural transduction
Lei Yu, Jan Buys, and Phil Blunsom. 2016 · 2016
Later among the works it cites.
Learning to generate one-sentence biographies from wikidata
Andrew Chisholm, Will Radford, and Ben Hachey. 2017 · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Speech and language processing
Dan Jurafsky and James H Martin. 2014 · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc VV Le. 2014 · 2014
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.
Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015
Cited alongside, same era.
Sequence-to-sequence generation for spoken dialogue via deep syntax trees and strings
Ondrej Dušek and Filip Jurcıcek. 2016 · 2016
Cited alongside, same era.
Hanjun Dai, Bo Dai, Yan-Ming Zhang, Shuang Li, and Le Song. 2017 · 2017
Later among the works it cites.
Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing. 2017 · 2017
Later among the works it cites.
The E2E dataset: New challenges for end-to-end generation
Jekaterina Novikova, Ondrej Dušek, and Verena Rieser. 2017 · 2017
Later among the works it cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
Later among the works it cites.
Style transfer from non-parallel text by cross-alignment
Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2017 · 2017
Later among the works it cites.
Sequence modeling via segmentations
Chong Wang, Yining Wang, Po-Sen Huang, Abdelrahman Mohamed, Dengyong Zhou, and Li Deng. 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.
Towards neural phrase-based machine translation
Po-Sen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, and Li Deng. 2018 · 2018
Closest in time.
Delete, retrieve, generate: A simple approach to sentiment and style transfer
J. Li, R. Jia, H. He, and P. Liang. 2018 · 2018
Closest in time.
Table-to-text generation by structure-aware seq2seq learning
Tianyu Liu, Kexiang Wang, Lei Sha, Baobao Chang, and Zhifang Sui. 2018 · 2018
Closest in time.
Neural language modeling by jointly learning syntax and lexicon
Yikang Shen, Zhouhan Lin, Chin wei Huang, and Aaron Courville. 2018 · 2018
Closest in time.
Breaking the softmax bottleneck: A high-rank RNN language model
Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, and William W. Cohen. 2018 · 2018
Closest in time.
Adversarially regularized autoencoders
Junbo Jake Zhao, Yoon Kim, Kelly Zhang, Alexander M. Rush, and Yann LeCun. 2018 · 2018
Closest in time.