Cross-Lingual Transfer for Machine Translation in Turkic Languages
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Computer Science > Computation and Language
Title:Cross-Lingual Transfer for Machine Translation in Turkic Languages
Abstract:Cross-lingual transfer is central to low-resource machine translation, but its behavior within closely related language families remains insufficiently characterized. We study transfer among five Turkic languages; Turkish, Azerbaijani, Uzbek, Kazakh, and Kyrgyz; using pairwise transfer matrices. In this setting, each model is fine-tuned with one transfer source and evaluated on a different transfer target while the translation target remains the same. Across mT5 experiments, we find that transfer is strongest between closely related Turkic pairs, especially Turkish-Azerbaijani and Kazakh-Kyrgyz. We also show that transfer direction matters, and that the same transfer source-transfer target pair can behave differently when the translation target changes. Latinization improves BLEU and chrF in several script-mismatched settings, but its effect is not uniform across metrics. Additional analyses show that transfer sources are mostly stable across different datasets and model settings.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.29355 [cs.CL] |
| (or arXiv:2607.29355v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.29355
arXiv-issued DOI via DataCite (pending registration)
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