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There is an emerging interest in the application of natural language processing models to source code processing tasks.
Visualizing high-dimensional data using t-sne
L.J.P. van der Maaten and G.E. Hinton. 2008 · 2008
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Pointing the unknown words
Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati, Bowen Zhou, and Yoshua Bengio. 2016 · 2016
Earlier work this paper cites.
Summarizing source code using a neural attention model
Srini Iyer, Ioannis Konstas, A. Cheung, and L. Zettlemoyer. 2016 · 2016
Earlier work this paper cites.
Probabilistic model for code with decision trees
Veselin Raychev, Pavol Bielik, and Martin Vechev. 2016a · 2016
Earlier work this paper cites.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Deepfix: Fixing common c language errors by deep learning
Rahul Gupta, Soham Pal, Aditya Kanade, and Shirish Shevade. 2017 · 2017
Earlier work this paper cites.
Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Compilation error repair: For the student programs, from the student programs
Umair Z. Ahmed, Pawan Kumar, Amey Karkare, Purushottam Kar, and Sumit Gulwani. 2018 · 2018
Cited alongside, same era.
Code completion with neural attention and pointer networks
Jian Li, Yue Wang, Michael R. Lyu, and Irwin King. 2018 · 2018
Cited alongside, same era.
Self-attention with relative position representations
Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani. 2018 · 2018
Cited alongside, same era.
The adverse effects of code duplication in machine learning models of code
Miltiadis Allamanis. 2019 · 2019
Cited alongside, same era.
code2seq: Generating sequences from structured representations of code
Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav. 2019a · 2019
Cited alongside, same era.
User2code2vec: Embeddings for profiling students based on distributional representations of source code
Novel positional encodings to enable tree-based transformers
Vighnesh Shiv and Chris Quirk. 2019 · 2019
Later among the works it cites.
Commit message generation for source code changes
Shengbin Xu, Yuan Yao, Feng Xu, Tianxiao Gu, Hanghang Tong, and Jian Lu. 2019 · 2019
Later among the works it cites.
Empirical study of transformers for source code
Nadezhda Chirkova and Sergey Troshin. 2020 · 2020
Closest in time.
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, and Ming Zhou. 2020 · 2020
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Global relational models of source code
Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, and David Bieber. 2020 · 2020
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David Azcona, Piyush Arora, I-Han Hsiao, and Alan Smeaton. 2019 · 2019
Cited alongside, same era.
Open vocabulary learning on source code with a graph-structured cache
Milan Cvitkovic, Badal Singh, and Animashree Anandkumar. 2019 · 2019
Cited alongside, same era.
Transformers and pointer-generator networks for abstractive summarization
Jon Deaton. 2019 · 2019
Cited alongside, same era.
code2vec: learning distributed representations of code
Uri Alon, Meital Zilberstein, Omer Levy, and E. Yahav. 2019b
Cited in the paper.
Learning programs from noisy data
Veselin Raychev, Pavol Bielik, Martin Vechev, and Andreas Krause. 2016b
Cited in the paper.
Closest in time.
Learning and evaluating contextual embedding of source code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi. 2020 · 2020
Closest in time.
Big code != big vocabulary: open-vocabulary models for source code
Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes, C. Sutton, and A. Janes. 2020 · 2020
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Code prediction by feeding trees to transformers
Seohyun Kim, Jinman Zhao, Yuchi Tian, and Satish Chandra. 2020 · 2020
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