CodeGen-Test: An Automatic Code Generation Model Integrating Program Test Information

Z. Sun, Q. Zhu, Y. Xiong, Y. Sun, L. Mou, and L. Zhang, “TreeGen: A Tree-Based Transformer Architecture for Code Generation,” AAAI 2020: 8984-8991 , Nov. 2019, arXiv: 1911.09983. [Online]. Available: http://arxiv.org/abs/1911.09983

Original

1911

Earlier work this paper cites.

R. J. Williams and D. Zipser, “A learning algorithm for continually running fully recurrent neural networks,” Neural computation , vol. 1, no. 2, pp. 270–280, 1989

1989

Earlier work this paper cites.

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997, publisher: MIT Press

1997

Earlier work this paper cites.

K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting of the Association for Computational Linguistics , 2002, pp. 311–318

2002

Earlier work this paper cites.

C.-Y. Lin, G. Cao, J. Gao, and J.-Y. Nie, “An information-theoretic approach to automatic evaluation of summaries,” in Proceedings of the Human Language Technology Conference of the NAACL, Main Conference , 2006, pp. 463–470

2006

Earlier work this paper cites.

L. Zettlemoyer and M. Collins, “Online learning of relaxed ccg grammars for parsing to logical form,” in Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) , 2007, pp. 678–687

2007

Earlier work this paper cites.

L. S. Zettlemoyer and M. Collins, “Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars,” arXiv preprint arXiv:1207.1420 , 2012

Original

2012

Earlier work this paper cites.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems , vol. 25, pp. 1097–1105, 2012

2012

Earlier work this paper cites.

N. Kushman and R. Barzilay, “Using semantic unification to generate regular expressions from natural language,” in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2013, pp. 826–836

2013

Earlier work this paper cites.

A. Wang, T. Kwiatkowski, and L. Zettlemoyer, “Morpho-syntactic lexical generalization for ccg semantic parsing,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2014, pp. 1284–1295

2014

Earlier work this paper cites.

I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in neural information processing systems , 2014, pp. 3104–3112

2014

Earlier work this paper cites.

K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078 , 2014

Original

2014

Earlier work this paper cites.

I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to Sequence Learning with Neural Networks,” in Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 , ser. NIPS’14. Cambridge, MA, USA: MIT Press, 2014, pp. 3104–3112, event-place: Montreal, Canada

2014

Earlier work this paper cites.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research , vol. 15, no. 1, pp. 1929–1958, 2014

2014

Earlier work this paper cites.

M.-T. Luong, Q. V. Le, I. Sutskever, O. Vinyals, and L. Kaiser, “Multi-task sequence to sequence learning,” arXiv preprint arXiv:1511.06114 , 2015

Original

2015

Cited alongside, same era.

L. Mou, R. Men, G. Li, L. Zhang, and Z. Jin, “On End-to-End Program Generation from User Intention by Deep Neural Networks,” 2015

2015

Cited alongside, same era.

W. Ling, P. Blunsom, E. Grefenstette, K. M. Hermann, T. Kočiský, F. Wang, and A. Senior, “Latent Predictor Networks for Code Generation,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Berlin, Germany: Association for Computational Linguistics, 2016, pp. 599–609. [Online]. Available: https://www.aclweb.org/anthology/P16-1057

2016

Cited alongside, same era.

L. Dong and M. Lapata, “Language to Logical Form with Neural Attention,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Berlin, Germany: Association for Computational Linguistics, 2016, pp. 33–43. [Online]. Available: https://www.aclweb.org/anthology/P16-1004

M. Dehghani, S. Gouws, O. Vinyals, J. Uszkoreit, and \. Kaiser, “Universal transformers,” arXiv preprint arXiv:1807.03819 , 2018

Original

2018

Later among the works it cites.

N. Shazeer and M. Stern, “Adafactor: Adaptive learning rates with sublinear memory cost,” in International Conference on Machine Learning . PMLR, 2018, pp. 4596–4604

Original

2018

Later among the works it cites.

Z. Sun, Q. Zhu, L. Mou, Y. Xiong, G. Li, and L. Zhang, “A grammar-based structural cnn decoder for code generation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 7055–7062, issue: 01

2019

Later among the works it cites.

B. Wei, G. Li, X. Xia, Z. Fu, and Z. Jin, “Code generation as a dual task of code summarization,” arXiv preprint arXiv:1910.05923 , 2019

Original

2019

Later among the works it cites.