Alexander Shypula


2023

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Explain-then-translate: an analysis on improving program translation with self-generated explanations
Zilu Tang | Mayank Agarwal | Alexander Shypula | Bailin Wang | Derry Wijaya | Jie Chen | Yoon Kim
Findings of the Association for Computational Linguistics: EMNLP 2023

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.