Automatic Code Generation using Pre-Trained Language Models

• URL: http://arxiv.org/abs/2102.10535v1
• Date: Sun, 21 Feb 2021 07:21:26 GMT
• Title: Automatic Code Generation using Pre-Trained Language Models
• Authors: Luis Perez, Lizi Ottens, Sudharshan Viswanathan
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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