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We propose a model to automatically describe changes introduced in the source code of a program using natural language.
Cognitive processes in program comprehension
Stanley Letovsky. 1987 · 1987
Earlier work this paper cites.
Building a large annotated corpus of english: The penn treebank
Mitchell P Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini. 1993 · 1993
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.
Minimum error rate training in statistical machine translation
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Earlier work this paper cites.
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Earlier work this paper cites.
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Nicola Bertoldi, Haddow Barry, and Jean-Baptiste Fouet. 2009 · 2009
Earlier work this paper cites.
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Hazeline U. Asuncion, Arthur U. Asuncion, and Richard N. Taylor. 2010 · 2010
Earlier work this paper cites.
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Raymond P.L. Buse and Westley R. Weimer. 2010 · 2010
Earlier work this paper cites.
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Earlier work this paper cites.
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Cited alongside, same era.
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Cited alongside, same era.
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Annibale Panichella, Bogdan Dit, Rocco Oliveto, Massimiliano Di Penta, Denys Poshyvanyk, and Andrea De Lucia. 2013 · 2013
Cited alongside, same era.
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Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
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Luis Fernando Cortés-Coy, Mario Linares Vásquez, Jairo Aponte, and Denys Poshyvanyk. 2014 · 2014
Cited alongside, same era.
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Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
Later among the works it cites.
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Veselin Raychev, Martin Vechev, and Andreas Krause. 2015 · 2015
Later among the works it cites.
Recurrent neural network regularization
Wojciech Zaremba, Ilya Sutskever, and Vinyals Oriol. 2015 · 2015
Later among the works it cites.
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Miltiadis Allamanis, Hao Peng, and Charles Sutton. 2016 · 2016
Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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