Fetching the paper…
Reading the bibliography…
A transcompiler, also known as source-to-source translator, is a system that converts source code from a high-level programming language (such as C++ or Python) to another.
Structural language models for any-code generation
Uri Alon, Roy Sadaka, Omer Levy, and Eran Yahav · 1910
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
Earlier work this paper cites.
Pharaoh: a beam search decoder for phrase-based statistical machine translation models
Philipp Koehn · 2004
Earlier work this paper cites.
Moses: Open source toolkit for statistical machine translation
Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Ondrej Bojar Chris Dyer, Alexandra Constantin, and Evan Herbst · 2007
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
Earlier work this paper cites.
Lexical statistical machine translation for language migration
Anh Tuan Nguyen, Tung Thanh Nguyen, and Tien N Nguyen · 2013
Earlier work this paper cites.
Learning natural coding conventions
Miltiadis Allamanis, Earl T Barr, Christian Bird, and Charles Sutton · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Phrase-based statistical translation of programming languages
Svetoslav Karaivanov, Veselin Raychev, and Martin Vechev · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
Earlier work this paper cites.
Using machine translation for converting python 2 to python 3 code
Karan Aggarwal, Mohammad Salameh, and Abram Hindle · 2015
Earlier work this paper cites.
On using monolingual corpora in neural machine translation
Caglar Gulcehre, Orhan Firat, Kelvin Xu, Kyunghyun Cho, Loic Barrault, Huei-Chi Lin, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2015
Earlier work this paper cites.
Learning to generate pseudo-code from source code using statistical machine translation (t)
Yusuke Oda, Hiroyuki Fudaba, Graham Neubig, Hideaki Hata, Sakriani Sakti, Tomoki Toda, and Satoshi Nakamura · 2015
Cited alongside, same era.
Learning python code suggestion with a sparse pointer network
Avishkar Bhoopchand, Tim Rocktäschel, Earl Barr, and Sebastian Riedel · 2016
Cited alongside, same era.
Dual learning for machine translation
Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma · 2016
Cited alongside, same era.
Neural attribute machines for program generation
Matthew Amodio, Swarat Chaudhuri, and Thomas Reps · 2017
Cited alongside, same era.
Learning bilingual word embeddings with (almost) no bilingual data
Mikel Artetxe, Gorka Labaka, and Eneko Agirre · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Later among the works it cites.
Deep code comment generation
Xing Hu, Ge Li, Xin Xia, David Lo, and Zhi Jin · 2018
Later among the works it cites.
Using recurrent neural networks for decompilation
Deborah S Katz, Jason Ruchti, and Eric Schulte · 2018
Later among the works it cites.
Taku Kudo and John Richardson · 2018
Later among the works it cites.
Code completion with neural attention and pointer networks
Jian Li, Yue Wang, Michael R Lyu, and Irwin King · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Antonio Valerio Miceli Barone and Rico Sennrich · 2017
Cited alongside, same era.
Deepfix: Fixing common c language errors by deep learning
Rahul Gupta, Soham Pal, Aditya Kanade, and Shirish Shevade · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Abstract syntax networks for code generation and semantic parsing
Maxim Rabinovich, Mitchell Stern, and Dan Klein · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Dynamic neural program embedding for program repair
Ke Wang, Rishabh Singh, and Zhendong Su · 2017
Cited alongside, same era.
A syntactic neural model for general-purpose code generation
Pengcheng Yin and Graham Neubig · 2017
Cited alongside, same era.
Sequencer: Sequence-to-sequence learning for end-to-end program repair
Zimin Chen, Steve James Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, and Martin Monperrus · 2019
Later among the works it cites.
Coda: An end-to-end neural program decompiler
Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, and Jishen Zhao · 2019
Later among the works it cites.
Two new evaluation datasets for low-resource machine translation: Nepali-english and sinhala-english
Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, and Marc’Aurelio Ranzato · 2019
Later among the works it cites.
Omer Katz, Yuval Olshaker, Yoav Goldberg, and Eran Yahav · 2019
Later among the works it cites.
Cross-lingual language model pretraining
Guillaume Lample and Alexis Conneau · 2019
Later among the works it cites.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 2019
Later among the works it cites.
Mass: Masked sequence to sequence pre-training for language generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu · 2019
Later among the works it cites.
Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2020
Closest in time.