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Text generation often requires high-precision output that obeys task-specific rules.
Unsupervised recurrent neural network grammars
Yoon Kim, Alexander M. Rush, Lei Yu, Adhiguna Kuncoro, Chris Dyer, and Gábor Melis. 2019 · 1904
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Multi-objects generation with amortized structural regularization
Kun Xu, Chongxuan Li, Jun Zhu, and Bo Zhang. 2019 · 1906
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The theory of segmental hidden markov models
M.J.F. Gales and Steve Young. 1993 · 1993
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Semiring parsing
Joshua Goodman. 1999 · 1999
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Bleu: A method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Semi-markov conditional random fields for information extraction
Sunita Sarawagi and William W Cohen. 2005 · 2005
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Comparing automatic and human evaluation of NLG systems
Anja Belz and Ehud Reiter. 2006 · 2006
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Meteor: An automatic metric for mt evaluation with high levels of correlation with human judgments
Alon Lavie and Abhaya Agarwal. 2007 · 2007
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First- and second-order expectation semirings with applications to minimum-risk training on translation forests
Zhifei Li and Jason Eisner. 2009 · 2009
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Conditional random field autoencoders for unsupervised structured prediction
Waleed Ammar, Chris Dyer, and Noah A. Smith. 2014 · 2014
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Weakly supervised detection with posterior regularization
Hakan Bilen, Marco Pedersoli, and Tinne Tuytelaars. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2014 · 2014
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling. 2014 · 2014
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Neural variational inference and learning in belief networks
Andriy Mnih and Karol Gregor. 2014 · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014 · 2014
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Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C. Lawrence Zitnick, and Devi Parikh. 2015 · 2015
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Sequence-to-sequence generation for spoken dialogue via deep syntax trees and strings
Ondrej Dusek and Filip Jurcicek. 2016 · 2016
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Sequence-to-sequence generation for spoken dialogue via deep syntax trees and strings
Ondřej Dušek and Filip Jurčíček. 2016 · 2016
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Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O.K. Li. 2016 · 2016
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Pointing the unknown words
Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati, Bowen Zhou, and Yoshua Bengio. 2016 · 2016
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Deep neural networks with massive learned knowledge
Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, and Eric Xing. 2016b · 2016
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Composing graphical models with neural networks for structured representations and fast inference
Generating syntactic paraphrases
Emilie Colin and Claire Gardent. 2018 · 2018
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Syntactic manipulation for generating more diverse and interesting texts
Jan Milan Deriu and Mark Cieliebak. 2018 · 2018
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Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
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Constituency parsing with a self-attentive encoder
Nikita Kitaev and Dan Klein. 2018 · 2018
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Table-to-text generation by structure-aware seq2seq learning
Tianyu Liu, Kexiang Wang, Lei Sha, Baobao Chang, and Zhifang Sui. 2018 · 2018
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Controlling personality-based stylistic variation with neural natural language generators
Shereen Oraby, Lena Reed, Shubhangi Tandon, Sharath T.S., Stephanie Lukin, and Marilyn Walker. 2018 · 2018
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Matthew Johnson, David K Duvenaud, Alex Wiltschko, Ryan P Adams, and Sandeep R Datta. 2016 · 2016
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Neural text generation from structured data with application to the biography domain
Rémi Lebret, David Grangier, and Michael Auli. 2016a · 2016
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Neural text generation from structured data with application to the biography domain
Rémi Lebret, David Grangier, and Michael Auli. 2016b · 2016
Cited alongside, same era.
What to talk about and how? selective generation using lstms with coarse-to-fine alignment
Hongyuan Mei, Mohit Bansal, and Matthew R. Walter. 2016 · 2016
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Variational inference for monte carlo objectives
Andriy Mnih and Danilo Rezende. 2016 · 2016
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Graph-based dependency parsing with bidirectional LSTM
Wenhui Wang and Baobao Chang. 2016 · 2016
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Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P. Xing. 2017 · 2017
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Towards controllable story generation
Nanyun Peng, Marjan Ghazvininejad, Jonathan May, and Kevin Knight. 2018 · 2018
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Learning neural templates for text generation
Sam Wiseman, Stuart Shieber, and Alexander Rush. 2018 · 2018
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Structvae: Tree-structured latent variable models for semi-supervised semantic parsing
Pengcheng Yin, Chunting Zhou, Junxian He, and Graham Neubig. 2018 · 2018
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Learning to control the specificity in neural response generation
Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Jun Xu, and Xueqi Cheng. 2018 · 2018
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Controllable paraphrase generation with a syntactic exemplar
Mingda Chen, Qingming Tang, Sam Wiseman, and Kevin Gimpel. 2019 · 2019
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Learning to control the fine-grained sentiment for story ending generation
Fuli Luo, Damai Dai, Pengcheng Yang, Tianyu Liu, Baobao Chang, Zhifang Sui, and Xu Sun. 2019 · 2019
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Step-by-step: Separating planning from realization in neural data-to-text generation
Amit Moryossef, Yoav Goldberg, and Ido Dagan. 2019 · 2019
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Pragmatically informative text generation
Sheng Shen, Daniel Fried, Jacob Andreas, and Dan Klein. 2019 · 2019
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Data-to-text generation with entity modeling
Ratish Puduppully, Li Dong, and Mirella Lapata. 2019 · 2035
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Posterior regularization for structured latent variable models
Kuzman Ganchev, João Graça, Jennifer Gillenwater, and Ben Taskar. 2010 · 2049
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