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Automatic Code Generation using Pre-Trained Language Models

2021-02-21 07:21:26
Luis Perez, Lizi Ottens, Sudharshan Viswanathan

Abstract

Recent advancements in natural language processing \cite{gpt2} \cite{BERT} have led to near-human performance in multiple natural language tasks. In this paper, we seek to understand whether similar techniques can be applied to a highly structured environment with strict syntax rules. Specifically, we propose an end-to-end machine learning model for code generation in the Python language built on-top of pre-trained language models. We demonstrate that a fine-tuned model can perform well in code generation tasks, achieving a BLEU score of 0.22, an improvement of 46\% over a reasonable sequence-to-sequence baseline. All results and related code used for training and data processing are available on GitHub.

Abstract (translated)

URL

https://arxiv.org/abs/2102.10535

PDF

https://arxiv.org/pdf/2102.10535.pdf


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