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Evaluation metrics play a vital role in the growth of an area as it defines the standard of distinguishing between good and bad models.
Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing
Guo, D.; Tang, D.; Duan, N.; Zhou, M.; and Yin, J. 2019 · 1906
Earlier work this paper cites.
Codesearchnet challenge: Evaluating the state of semantic code search
Husain, H.; Wu, H.-H.; Gazit, T.; Allamanis, M.; and Brockschmidt, M. 2019 · 1909
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Translation
Weaver, W. 1955 · 1955
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Pre-trained contextual embedding of source code
Kanade, A.; Maniatis, P.; Balakrishnan, G.; and Shi, K. 2019 · 2001
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Codebert: A pre-trained model for programming and natural languages
Feng, Z.; Guo, D.; Tang, D.; Duan, N.; Feng, X.; Gong, M.; Shou, L.; Qin, B.; Liu, T.; Jiang, D.; et al. 2020 · 2002
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BLEU: a method for automatic evaluation of machine translation
Papineni, K.; Roukos, S.; Ward, T.; and Zhu, W.-J. 2002 · 2002
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ROUGE: A Package for Automatic Evaluation of Summaries
Lin, C.-Y. 2004 · 2004
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IntelliCode Compose: Code Generation Using Transformer
Svyatkovskiy, A.; Deng, S. K.; Fu, S.; and Sundaresan, N. 2020 · 2005
Earlier work this paper cites.
Unsupervised Translation of Programming Languages
Lachaux, M.-A.; Roziere, B.; Chanussot, L.; and Lample, G. 2020 · 2006
Earlier work this paper cites.
GraphCodeBERT: Pre-training Code Representations with Data Flow
Guo, D.; Ren, S.; Lu, S.; Feng, Z.; Tang, D.; Liu, S.; Zhou, L.; Duan, N.; Yin, J.; Jiang, D.; et al. 2020 · 2009
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Bahdanau, D.; Cho, K.; and Bengio, Y. 2014 · 2014
Cited alongside, same era.
Phrase-based statistical translation of programming languages
Karaivanov, S.; Raychev, V.; and Vechev, M. 2014 · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Sutskever, I.; Vinyals, O.; and Le, Q. V. 2014 · 2014
Cited alongside, same era.
Bimodal modelling of source code and natural language
Allamanis, M.; Tarlow, D.; Gordon, A.; and Wei, Y. 2015 · 2015
Cited alongside, same era.
Divide-and-conquer approach for multi-phase statistical migration for source code (t)
Nguyen, A. T.; Nguyen, T. T.; and Nguyen, T. N. 2015 · 2015
Cited alongside, same era.
Learning to generate pseudo-code from source code using statistical machine translation (t)
A survey of machine learning for big code and naturalness
Allamanis, M.; Barr, E. T.; Devanbu, P.; and Sutton, C. 2018 · 2018
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Tree-to-tree neural networks for program translation
Chen, X.; Liu, C.; and Song, D. 2018 · 2018
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Mapping Language to Code in Programmatic Context
Iyer, S.; Konstas, I.; Cheung, A.; and Zettlemoyer, L. 2018 · 2018
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Automatic software repair: a bibliography
Monperrus, M. 2018 · 2018
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code2vec: Learning distributed representations of code
Alon, U.; Zilberstein, M.; Levy, O.; and Yahav, E. 2019 · 2019
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Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; and Sutskever, I. 2019 · 2019
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Oda, Y.; Fudaba, H.; Neubig, G.; Hata, H.; Sakti, S.; Toda, T.; and Nakamura, S. 2015 · 2015
Cited alongside, same era.
Barone, A. V. M.; and Sennrich, R. 2017 · 2017
Cited alongside, same era.
Abstract syntax networks for code generation and semantic parsing
Rabinovich, M.; Stern, M.; and Klein, D. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
A Syntactic Neural Model for General-Purpose Code Generation
Yin, P.; and Neubig, G. 2017 · 2017
Cited alongside, same era.
An empirical study on learning bug-fixing patches in the wild via neural machine translation
Tufano, M.; Watson, C.; Bavota, G.; Penta, M. D.; White, M.; and Poshyvanyk, D. 2019 · 2019
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Sequence generation: From both sides to the middle
Zhou, L.; Zhang, J.; Zong, C.; and Yu, H. 2019 · 2019
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Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs
Dinella, E.; Dai, H.; Li, Z.; Naik, M.; Song, L.; and Wang, K. 2020 · 2020
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