Improving Low-Resource J apanese Translation with Fine-Tuning and Backtranslation for the WMT 25 General Translation Task (2025)

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→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.

## introduction neural mt for japanese benefits from recent large language models (llms) and recipe-driven data augmentation, but publicly documented, smallcorpus workflows are scarce. this paper focuses on a minimalist, engineering-first pipeline that couples (i) supervised fine-tuning (ft) on a small japanese corpus with (ii) backtranslation (bt) to expand coverage. our objectives are: ## related work research on improving neural machine translation (nmt) for japanese has increasingly relied on two complementary techniques: backtranslation and fine-tuning. early large-scale systems demonstrated that backtranslation is particularly effective for low-resource settings, as it leverages abundant monolingual corpora to generate synthetic parallel data. this method augments scarce bilingual datasets and helps reduce domain mismatch, which is a persistent challenge in english-japanese translation. @xcite investigated english-japanese news translation at wmt 2020, showing that the combination of synthetic data through backtranslation and subsequent fine-tuning significantly improved performance over a baseline. extending this line of work, @xcite explored finetuning with domain-specific corpora and demonstrated that backtranslation enhanced adaptation to the news domain in the wmt 2021 shared task. their study highlighted the importance of tailoring fine-tuning schedules when working with japanese corpora. further refinements were presented by @xcite in wmt 2022, who introduced a system that incorporated both extensive backtranslation and selective fine-tuning. their approach confirmed that even moderate-scale synthetic corpora, when carefully integrated, yield measurable improvements in translation accuracy for japanese. similarly, @xcite reported results from wmt 2023 where backtranslation and iterative fine-tuning were applied to robustly adapt transformer-based systems, demonstrating strong gains for english-japanese translation. in parallel, multilingual nmt research has also highlighted the value of backtranslation. @xcite proposed an auxiliary language framework, leveraging backtranslation across multiple lan-guage pairs, including japanese. their results suggest that cross-lingual signals derived from backtranslation not only improve individual language directions but also enhance multilingual consistency. these studies illustrate the central role of backtranslation in augmenting limited japanese corpora and show that fine-tuning, when combined with synthetic data, can consistently raise translation quality. they provide the empirical foundation for our own work for low-resource japanese nmt. ## system architecture our proposed method combines fine-tuning on a small parallel japanese-english dataset with backtranslation to augment the available training data. as illustrated in figure 1 , monolingual japanese sentences are first translated into english using the a pretrained model to create synthetic parallel pairs. these synthetic pairs are then used with the original data to fine-tune a pretrained model. the resulting system benefits from both the linguistic diversity of backtranslation and the domain adaptation of fine-tuning, leading to improved translation quality as measured by comet, bleu, and chrf++. ## conclusion in this work, we investigated the combined use of fine-tuning (ft) and backtranslation (bt) to improve english-japanese neural machine translation under small-data conditions. the results show that parameter-efficient fine-tuning combined with carefully filtered backtranslation can provide a practical and effective blueprint for improving japanese translation, even with limited computational resources. future work will explore domain adaptation and scaling synthetic data generation to further enhance robustness.