mt5

This paper studies cross-lingual transfer for machine translation among five Turkic languages using pairwise transfer matrices with mT5, finding that transfer is strongest between closely related pairs and that Latinization helps in script-mismatched settings.

Presents a neural machine translation system for the severely under-resourced Tangkhul–English language pair, achieving strong BLEU, chrF++, BERTScore, and COMET scores using fine-tuned ByT5-large and mT5-small models.