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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 3·5 min read
Cross-Lingual Transfer for Machine Translation in Turkic Languages
TL;DR
Cross-lingual transfer among Turkic languages enhances machine translation, revealing strong transfer effects particularly between closely related pairs like Turkish-Azerbaijani. The study highlights the importance of transfer direction and the impact of Latinization on translation metrics.
✦ Why It Matters
Consider implementing cross-lingual transfer techniques to enhance your machine translation systems for Turkic languages.
Key Takeaways
- Explore pairwise transfer matrices for language pairs in your projects.
- Utilize mT5 for effective fine-tuning in low-resource settings.
- Assess transfer direction to optimize translation outcomes.
- Experiment with Latinization to improve translation metrics.
Full Summary
Cross-lingual transfer is crucial for machine translation, especially in low-resource languages. This study investigates transfer among five Turkic languages: Turkish, Azerbaijani, Uzbek, Kazakh, and Kyrgyz, using pairwise transfer matrices.
The researchers fine-tuned models on one language and evaluated them on another while keeping the translation target constant. Results from mT5 experiments indicate that transfer is most effective between closely related languages, particularly Turkish-Azerbaijani and Kazakh-Kyrgyz.
Additionally, the direction of transfer influences outcomes, with variations observed when changing translation targets. Latinization, the process of converting scripts, improved BLEU and chrF scores in several mismatched script scenarios, though its effects varied across metrics.
Overall, transfer sources remained stable across different datasets and model configurations.