No One-Size-Fits-All: Building Systems For Translation to Bashkir, Kazakh, Kyrgyz, Tatar and Chuvash Using Synthetic And Original Data
Abstract
Machine translation experiments for Turkic languages using nllb-200, LoRA fine-tuning, and prompt-based approaches achieved varying chrF++ scores across language pairs.
We explore machine translation for five Turkic language pairs: Russian-Bashkir, Russian-Kazakh, Russian-Kyrgyz, English-Tatar, English-Chuvash. Fine-tuning nllb-200-distilled-600M with LoRA on synthetic data achieved chrF++ 49.71 for Kazakh and 46.94 for Bashkir. Prompting DeepSeek-V3.2 with retrieved similar examples achieved chrF++ 39.47 for Chuvash. For Tatar, zero-shot or retrieval-based approaches achieved chrF++ 41.6, while for Kyrgyz the zero-shot approach reached 45.6. We release the dataset and the obtained weights.
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We show that effective machine translation for low-resource Turkic languages requires a tailored approach: fine-tuning works best for languages with some data, while retrieval-augmented LLM prompting is essential for extremely resource-scarce ones.
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