Exploring Parameter-Efficient Fine-Tuning and Backtranslation for the WMT 25 General Translation Task

• URL: http://arxiv.org/abs/2511.12109v1
• Date: Sat, 15 Nov 2025 08:59:18 GMT
• Title: Exploring Parameter-Efficient Fine-Tuning and Backtranslation for the WMT 25 General Translation Task
• Abstract summary: We show that the synergistic use of backtranslation and fine-tuning on Japanese corpora can significantly enhance translation quality.<n>This approach offers a lightweight yet powerful strategy for improving low-resource language pairs.
• Score: 0.12891210250935145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we explore the effectiveness of combining fine-tuning and backtranslation on a small Japanese corpus for neural machine translation. Starting from a baseline English{\textrightarrow}Japanese model (COMET = 0.460), we first apply backtranslation (BT) using synthetic data generated from monolingual Japanese corpora, yielding a modest increase (COMET = 0.468). Next, we fine-tune (FT) the model on a genuine small parallel dataset drawn from diverse Japanese news and literary corpora, achieving a substantial jump to COMET = 0.589 when using Mistral 7B. Finally, we integrate both backtranslation and fine-tuning{ -- }first augmenting the small dataset with BT generated examples, then adapting via FT{ -- }which further boosts performance to COMET = 0.597. These results demonstrate that, even with limited training data, the synergistic use of backtranslation and targeted fine-tuning on Japanese corpora can significantly enhance translation quality, outperforming each technique in isolation. This approach offers a lightweight yet powerful strategy for improving low-resource language pairs.

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