ArticleFrontiers in artificial intelligence2026
Automatic transformation of Kazakh text into sign language glosses using multilingual transformer-based models.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study investigates the automatic transformation of Kazakh text into sign language glosses (Text-to-Gloss) using multilingual transformer-based models with emphasis on preserving morphological structure in a low-resource agglutinative language framework. Given the scarcity of high-quality intermediate representations for Kazakh Sign Language, a methodology for corpus formation was developed, resulting in a specialized dataset of 11 190 unique text-gloss pairs sourced from educational materials. To assess the effectiveness of transfer learning under low-resource constraints, we fine-tuned mT5-small and mT5-base models on this dataset. Experimental results indicate that the mT5-base architecture consistently outperforms the smaller variant across all metrics, achieving a 2.61-point improvement in BLEU (87.51 → 90.12) and a 2.47-point increase in ROUGE-1 F1 (89.61% → 92.08%), alongside gains in exact match (74.96% → 80.68%) and chrF (91.6 → 94.1). Statistical significance testing (
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