GANCoder: An Automatic Natural Language-to-Programming Language Translation Approach based on GAN
Table 2: Accuracy comparison of different models
['Models', 'ATIS', 'Django', 'Jobs']
Compared to the traditional Encoder-Decoder models, such as SEQ2SEQ and SEQ2TREE, GANCoder increases the accuracy by 24.6% and 30.3% on the Django dataset. These two models work better on the Jobs dataset, but the results on the other two datasets are not satisfactory. The ASN model achieves the best performance on the ATIS dataset but cannot obtain results on the other two datasets. The LPN+COPY model and the SNM+COPY model show good results on the Django dataset, but they also do not have results on the other two datasets. Although the GANCoder proposed in this paper is not always the best compared with the other models, GANCoder can achieve relatively stable and satisfactory results on various datasets and is a promising code generation method worth improving.