Open AccessGCNGAN: Translating Natural Language to Programming Language based on GAN
Author(s) -
Hongming Dai,
Chen Chen,
Yunjing Li,
Yanghao Yuan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1873/1/012070
Subject(s) - computer science , discriminator , artificial intelligence , parsing , encoder , natural language , graph , natural language processing , generalization , domain (mathematical analysis) , programming language , generative grammar , theoretical computer science , telecommunications , mathematical analysis , mathematics , detector , operating system
Cross-language translating has been well solved with the help of the processing of natural language processing(NLP). However, there are a few studies done about the domain of translating the natural language to programming snippets. Traditional method are mostly rule-based and limited to specific areas. In this paper, we propose a model based on Graph Convolutional Network(GCN)-Generative Adversarial Networks(GAN) to translate natural language to programming language. The generator is the encoder-decoder framework in which the encoder and the decoder are all bidirectional RNNs and GCN. And discriminator in GAN is also bidirectional RNNs and GCN. To improve the performance of semantic parsing, we also apply the attention mechanism to it. The experimental results indicate that our model has achieved comparable performance with some other state-of-the-art methods and has the stronger generalization ability.