Data Augmentation for Code Translation with Comparable Corpora and Multiple References (2023.findings-emnlp)
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| Challenge: | Existing methods for translating code between programming languages are limited by parallel training data. |
| Approach: | They propose a data augmentation technique that builds comparable corpora and augments existing parallel data with multiple reference translations. |
| Outcome: | The proposed techniques improve CodeT5 translation between Java, Python, and C++ by an average of 7.5% Computational Accuracy (CA@1) . |
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