Natural Language to Code Using Transformers

02/01/2022
by   Uday Kusupati, et al.
0

We tackle the problem of generating code snippets from natural language descriptions using the CoNaLa dataset. We use the self-attention based transformer architecture and show that it performs better than recurrent attention-based encoder decoder. Furthermore, we develop a modified form of back translation and use cycle consistent losses to train the model in an end-to-end fashion. We achieve a BLEU score of 16.99 beating the previously reported baseline of the CoNaLa challenge.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset