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Paper detail

Natural Language to Code Using Transformers

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.

preprint2022arXivOpen access
Uday KusupatiVenkata Ravi Teja Ailavarapu
Computation and LanguageMachine Learning
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Uday KusupatiVenkata Ravi Teja Ailavarapu

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