@inproceedings{tang-etal-2023-explain,
title = "Explain-then-translate: an analysis on improving program translation with self-generated explanations",
author = "Tang, Zilu and
Agarwal, Mayank and
Shypula, Alexander and
Wang, Bailin and
Wijaya, Derry and
Chen, Jie and
Kim, Yoon",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.119",
doi = "10.18653/v1/2023.findings-emnlp.119",
pages = "1741--1788",
abstract = "This work explores the use of self-generated natural language explanations as an intermediate step for code-to-code translation with language models. Across three types of explanations and 19 programming languages constructed from the MultiPL-E dataset, we find the explanations to be particularly effective in the zero-shot case, improving performance by 12{\%} on average. Improvements with natural language explanations are particularly pronounced on difficult programs. We release our dataset, code, and canonical solutions in all 19 languages.",
}
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<abstract>This work explores the use of self-generated natural language explanations as an intermediate step for code-to-code translation with language models. Across three types of explanations and 19 programming languages constructed from the MultiPL-E dataset, we find the explanations to be particularly effective in the zero-shot case, improving performance by 12% on average. Improvements with natural language explanations are particularly pronounced on difficult programs. We release our dataset, code, and canonical solutions in all 19 languages.</abstract>
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%0 Conference Proceedings
%T Explain-then-translate: an analysis on improving program translation with self-generated explanations
%A Tang, Zilu
%A Agarwal, Mayank
%A Shypula, Alexander
%A Wang, Bailin
%A Wijaya, Derry
%A Chen, Jie
%A Kim, Yoon
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F tang-etal-2023-explain
%X This work explores the use of self-generated natural language explanations as an intermediate step for code-to-code translation with language models. Across three types of explanations and 19 programming languages constructed from the MultiPL-E dataset, we find the explanations to be particularly effective in the zero-shot case, improving performance by 12% on average. Improvements with natural language explanations are particularly pronounced on difficult programs. We release our dataset, code, and canonical solutions in all 19 languages.
%R 10.18653/v1/2023.findings-emnlp.119
%U https://aclanthology.org/2023.findings-emnlp.119
%U https://doi.org/10.18653/v1/2023.findings-emnlp.119
%P 1741-1788
Markdown (Informal)
[Explain-then-translate: an analysis on improving program translation with self-generated explanations](https://aclanthology.org/2023.findings-emnlp.119) (Tang et al., Findings 2023)
ACL