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Learning to generate fluent natural language from structured data with neural networks has become an common approach for NLG.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Stochastic language generation for spoken dialogue systems
Alice H Oh and Alexander I Rudnicky. 2000 · 2000
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Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
George Doddington. 2002 · 2002
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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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Trainable sentence planning for complex information presentation in spoken dialog systems
Amanda Stent, Rashmi Prasad, and Marilyn Walker. 2004 · 2004
Earlier work this paper cites.
Individual and domain adaptation in sentence planning for dialogue
Marilyn A Walker, Amanda Stent, François Mairesse, and Rashmi Prasad. 2007 · 2007
Earlier work this paper cites.
Multiple choice learning: Learning to produce multiple structured outputs
Abner Guzman-Rivera, Dhruv Batra, and Pushmeet Kohli. 2012 · 2012
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A statistical nlg framework for aggregated planning and realization
Ravi Kondadadi, Blake Howald, and Frank Schilder. 2013 · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Meteor universal: Language specific translation evaluation for any target language
Michael Denkowski and Alon Lavie. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2014 · 2014
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Stochastic language generation in dialogue using factored language models
François Mairesse and Steve Young. 2014 · 2014
Earlier work this paper cites.
Efficient elicitation of annotations for human evaluation of machine translation
Keisuke Sakaguchi, Matt Post, and Benjamin Van Durme. 2014 · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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.
What to talk about and how? selective generation using lstms with coarse-to-fine alignment
Hongyuan Mei, Mohit Bansal, and Matthew R Walter. 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.
Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015 · 2015
Cited alongside, same era.
Linguistic realisation as machine translation: Comparing different mt models for amr-to-text generation
Thiago Castro Ferreira, Iacer Calixto, Sander Wubben, and Emiel Krahmer. 2017 · 2017
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Opennmt: Open-source toolkit for neural machine translation
Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M Rush. 2017 · 2017
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Neural amr: Sequence-to-sequence models for parsing and generation
Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, and Luke Zettlemoyer. 2017 · 2017
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Confident multiple choice learning
Kimin Lee, Changho Hwang, KyoungSoo Park, and Jinwoo Shin. 2017 · 2017
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Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic, Pei-Hao Su, David Vandyke, and Steve Young. 2015 · 2015
Cited alongside, same era.
Sequence-to-sequence generation for spoken dialogue via deep syntax trees and strings
Ondřej Dušek and Filip Jurčíček. 2016 · 2016
Cited alongside, same era.
Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati, Bowen Zhou, and Yoshua Bengio. 2016 · 2016
Cited alongside, same era.
Imitation learning for language generation from unaligned data
Gerasimos Lampouras and Andreas Vlachos. 2016 · 2016
Cited alongside, same era.
Stochastic multiple choice learning for training diverse deep ensembles
Stefan Lee, Senthil Purushwalkam Shiva Prakash, Michael Cogswell, Viresh Ranjan, David Crandall, and Dhruv Batra. 2016 · 2016
Cited alongside, same era.
Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Caglar Gulcehre, Bing Xiang, et al. 2016 · 2016
Cited alongside, same era.
Modeling coverage for neural machine translation
Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li. 2016 · 2016
Cited alongside, same era.
Jekaterina Novikova, Ondrej Dušek, and Verena Rieser. 2017 · 2017
Later among the works it cites.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
Later among the works it cites.
Addressing the data sparsity issue in neural amr parsing
Xiaochang Peng, Chuan Wang, Daniel Gildea, and Nianwen Xue. 2017 · 2017
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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.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Later among the works it cites.
Findings of the E2E NLG challenge
Ondrej Dušek, Jekaterina Novikova, and Verena Rieser. 2018 · 2018
Closest in time.
Unsupervised natural language generation with denoising autoencoders
Markus Freitag and Scott Roy. 2018 · 2018
Closest in time.
A deep ensemble model with slot alignment for sequence-to-sequence natural language generation
Juraj Juraska, Panagiotis Karagiannis, Kevin Bowden, and Marilyn Walker. 2018 · 2018
Closest in time.
Natural language generation by hierarchical decoding with linguistic patterns
Shang-Yu Su, Kai-Ling Lo, Yi Ting Yeh, and Yun-Nung Chen. 2018 · 2018
Closest in time.