Papers › MarianCG: a code generation transformer model inspired by machine translation

MarianCG: a code generation transformer model inspired by machine translation

22 Nov 2022Journal of Engineering and Applied Science 2022 11archive 2025-07-28

Ahmed S. Soliman, Mayada M. Hadhoud, Samir I. Shaheen

The idea that computers can build their own programs is extremely significant, and many researchers are working on this challenge. Code generation is described as the process of generating executable code that can be run directly on the computer and fulfills the natural language requirements. It is an intriguing topic that might assist developers to learn a new software technology or programming language, or it could be a simple technique to help in coding through the description of the natural language code developer. In this paper, we present MarianCG, a code generation Transformer model used to tackle the code generation challenge of generating python code from natural language descriptions. Marian neural machine translation (NMT), which is the core model of the Microsoft Translator, is the basis for our NL-to-Code translation engine and is the heart of the teaching model. MarianMT is the teacher language model in our study, and it is one of the most successful machine translation transformers. In our approach, we use a sinusoidal positional embedding technique to represent the position of each token in the text, as well as no layer normalization embedding. Our code generation approach, MarianCG, is based on fine-tuning a machine translation pre-trained language model. This allows us to demonstrate that the pre-trained translation model can also operate and work as a code generation model. The proposed model outperforms recent state-of-the-art models in the problem of code generation when trained on the CoNaLa and DJANGO datasets. MarianCG model scores a BLEU score of 34.43 and an exact match accuracy of 10.2% on the CoNaLa dataset. Also, this model records a BLEU score of 90.41 and an exact match accuracy of 81.83% on the DJANGO dataset. The implementation of MarianCG model and relevant resources are available at https://www.github.com/AhmedSSoliman/MarianCG-NL-to-Code.

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AhmedSSoliman/MarianCG-NL-to-Code officialmentioned in paper report

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Tasks

Code Generation Code Translation Language Modeling Language Modelling Machine Translation NMT Translation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Code Generation	CoNaLa	MarianCG	BLEU	34.43	#3 of 14	Archive leaderboard	report
Code Generation	CoNaLa	MarianCG	Exact Match Accuracy	10.2	#3 of 14	Archive leaderboard	report
Code Generation	Django	MarianCG	Accuracy	81.83	#1 of 11	Archive leaderboard	report
Code Generation	Django	MarianCG	BLEU Score	90.41	#1 of 11	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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