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Neural machine translation (NMT) methods developed for natural language processing have been shown to be highly successful in automating translation from one natural language to another.
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Addressing the rare word problem in neural machine translation
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Empirical studies on the nlp techniques for source code data preprocessing
Xiaobing Sun, Xiangyue Liu, Jiajun Hu, and Junwu Zhu · 2014
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Jeffrey Svajlenko, Judith F Islam, Iman Keivanloo, Chanchal K Roy, and Mohammad Mamun Mia · 2014
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An exact graph edit distance algorithm for solving pattern recognition problems
Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel, and Patrick Martineau · 2015
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Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
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Synthesizing data structure transformations from input-output examples
John K Feser, Swarat Chaudhuri, and Isil Dillig · 2015
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Character-based neural machine translation
Wang Ling, Isabel Trancoso, Chris Dyer, and Alan W Black · 2015
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning · 2015
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba · 2015
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Scott Reed and Nando De Freitas · 2015
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Improved semantic representations from tree-structured long short-term memory networks
Kai Sheng Tai, Richard Socher, and Christopher D Manning · 2015
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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Long short-term memory over recursive structures
Xiaodan Zhu, Parinaz Sobihani, and Hongyu Guo · 2015
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Matej Balog, Alexander L Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Daniel Tarlow · 2016
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Automated correction for syntax errors in programming assignments using recurrent neural networks
Sahil Bhatia and Rishabh Singh · 2016
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Learning python code suggestion with a sparse pointer network
Avishkar Bhoopchand, Tim Rocktäschel, Earl Barr, and Sebastian Riedel · 2016
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A character-level decoder without explicit segmentation for neural machine translation
Junyoung Chung, Kyunghyun Cho, and Yoshua Bengio · 2016
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Hybrid computing using a neural network with dynamic external memory
Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, et al · 2016
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Jiatao Gu, Zhengdong Lu, Hang Li, and Victor OK Li · 2016
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Abram Hindle, Earl T Barr, Mark Gabel, Zhendong Su, and Premkumar Devanbu · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Latent predictor networks for code generation
Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiskỳ, Andrew Senior, Fumin Wang, and Phil Blunsom · 2016
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Neural code completion
Chang Liu, Xin Wang, Richard Shin, Joseph E Gonzalez, and Dawn Song · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Convolutional neural networks over tree structures for programming language processing
Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin · 2016
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Veselin Raychev, Pavol Bielik, and Martin Vechev · 2016
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Roee Aharoni and Yoav Goldberg · 2017
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Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi · 2017
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Antonio Valerio Miceli Barone and Rico Sennrich · 2017
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Matko Bošnjak, Tim Rocktäschel, Jason Naradowsky, and Sebastian Riedel · 2017
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Copied monolingual data improves low-resource neural machine translation
Anna Currey, Antonio Valerio Miceli-Barone, and Kenneth Heafield · 2017
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Jacob Devlin, Jonathan Uesato, Rishabh Singh, and Pushmeet Kohli · 2017
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Beam search strategies for neural machine translation
Markus Freitag and Yaser Al-Onaizan · 2017
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Jian Li, Yue Wang, Michael R Lyu, and Irwin King · 2017
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Program synthesis from natural language using recurrent neural networks
Xi Victoria Lin, Chenglong Wang, Deric Pang, Kevin Vu, and Michael D Ernst · 2017
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Maxim Rabinovich, Mitchell Stern, and Dan Klein · 2017
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Abigail See, Peter J Liu, and Christopher D Manning · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al · 2020
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Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
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Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al · 2021
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Pengcheng Yin and Graham Neubig · 2017
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Victor Zhong, Caiming Xiong, and Richard Socher · 2017
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A survey of machine learning for big code and naturalness
Miltiadis Allamanis, Earl T Barr, Premkumar Devanbu, and Charles Sutton · 2018
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code2seq: Generating sequences from structured representations of code
Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav · 2018
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pix2code: Generating code from a graphical user interface screenshot
Tony Beltramelli · 2018
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Treebert: A tree-based pre-trained model for programming language
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Neutron: an attention-based neural decompiler
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Between words and characters: A brief history of open-vocabulary modeling and tokenization in nlp
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Codenet: A large-scale ai for code dataset for learning a diversity of coding tasks
Ruchir Puri, David S Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladimir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, et al · 2021
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