Abstract
We propose GANCoder, an automatic programming approach based on Generative Adversarial Networks (GAN), which can generate the same functional and logical programming language codes conditioned on the given natural language utterances. The adversarial training between generator and discriminator helps generator learn distribution of dataset and improve code generation quality. Our experimental results show that GANCoder can achieve comparable accuracy with the state-of-the-art methods and is more stable when programming languages.
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Acknowledgements
This work was partially supported by National Key R&D Program of China (2018YFB1003404), National Natural Science Foundation of China (61672141), and Fundamental Research Funds for the Central Universities (N181605017).
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Zhu, Y., Zhang, Y., Yang, H., Wang, F. (2019). GANCoder: An Automatic Natural Language-to-Programming Language Translation Approach Based on GAN. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_48
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DOI: https://doi.org/10.1007/978-3-030-32236-6_48
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