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Data-to-text generation can be conceptually divided into two parts: ordering and structuring the information (planning), and generating fluent language describing the information (realization).
Binary codes capable of correcting deletions, insertions, and reversals
Vladimir I Levenshtein. 1966 · 1966
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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A brief introduction to boosting
Robert E Schapire. 1999 · 1999
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Building natural language generation systems
Ehud Reiter and Robert Dale. 2000 · 2000
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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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Probabilistic text structuring: Experiments with sentence ordering
Mirella Lapata. 2003 · 2003
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Nltk: the natural language toolkit
Steven Bird and Edward Loper. 2004 · 2004
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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
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie. 2005 · 2005
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Aggregation via set partitioning for natural language generation
Regina Barzilay and Mirella Lapata. 2006 · 2006
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Unsupervised concept-to-text generation with hypergraphs
Ioannis Konstas and Mirella Lapata. 2012 · 2012
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Inducing document plans for concept-to-text generation
Ioannis Konstas and Mirella Lapata. 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
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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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
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Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015
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Semantically conditioned lstm-based natural language generation for spoken dialogue systems
Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic, Pei-Hao Su, David Vandyke, and Steve Young. 2015 · 2015
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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Forge at semeval-2017 task 9: Deep sentence generation based on a sequence of graph transducers
Simon Mille, Roberto Carlini, Alicia Burga, and Leo Wanner. 2017 · 2017
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Shikhar Sharma, Layla El Asri, Hannes Schulz, and Jeremie Zumer. 2017 · 2017
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Style transfer from non-parallel text by cross-alignment
Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2017 · 2017
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Challenges in data-to-document generation
Sam Wiseman, Stuart M Shieber, and Alexander M Rush. 2017 · 2017
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The webnlg challenge: Generating text from dbpedia data
Emilie Colin, Claire Gardent, Yassine Mrabet, Shashi Narayan, and Laura Perez-Beltrachini. 2016 · 2016
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Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati, Bowen Zhou, and Yoshua Bengio. 2016 · 2016
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Globally coherent text generation with neural checklist models
Chloé Kiddon, Luke Zettlemoyer, and Yejin Choi. 2016 · 2016
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Controlling linguistic style aspects in neural language generation
Jessica Ficler and Yoav Goldberg. 2017 · 2017
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The webnlg challenge: Generating text from rdf data
Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini. 2017 · 2017
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Albert Gatt and Emiel Krahmer. 2017 · 2017
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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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Adversarially regularized autoencoders for generating discrete structures
Junbo Jake Zhao, Yoon Kim, Kelly Zhang, Alexander M. Rush, and Yann LeCun. 2017 · 2017
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Findings of the e2e nlg challenge
Ondřej Dušek, Jekaterina Novikova, and Verena Rieser. 2018 · 2018
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Neuralreg: An end-to-end approach to referring expression generation
Thiago Castro Ferreira, Diego Moussallem, Ákos Kádár, Sander Wubben, and Emiel Krahmer. 2018 · 2018
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Delete, retrieve, generate: A simple approach to sentiment and style transfer
Juncen Li, Robin Jia, He He, and Percy Liang. 2018 · 2018
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Data-to-text generation with content selection and planning
Ratish Puduppully, Li Dong, and Mirella Lapata. 2018 · 2018
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E2e nlg challenge: Neural models vs. templates
Yevgeniy Puzikov and Iryna Gurevych. 2018 · 2018
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Learning neural templates for text generation
Sam Wiseman, Stuart M Shieber, and Alexander M Rush. 2018 · 2018
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