Script Correction and Synthetic Pivoting: Adapting Tencent HY-MT for Low-Resource Turkic Translation
Year: 2026
Type: conference-paper
Abstract: This paper describes a submission to the LoResMT 2026 Shared Task for the Russian-Kazakh, Russian-Bashkir, and English-Chuvash tracks.The primary approach involves parameter-efficient fine-tuning (LoRA) of the Tencent HY-MT1.5-7Bmultilingual model.For the Russian-Kazakh and Russian-Bashkir pairs, LoRA adaptation was employed to correct the model's default Arabic script output to Cyrillic.For the extremely lowresource English-Chuvash pair, two strategies were compared: mixed training on authentic English-Chuvash and Russian-Chuvash data versus training exclusively on a synthetic English-Chuvash corpus created via pivoting through Russian.Baseline systems included NLLB 1.3B (distilled) for Russian-Kazakh and Russian-Bashkir, and Gemma 2 3B for English-Chuvash.Results demonstrate that adapting a strong multilingual backbone with LoRA yields significant improvements over baselines while successfully addressing script mismatch challenges.Code for training and inference is released at: https://github.com/ defdet/low-resource-langs-mt-adapt
Author: Bolgov Maxim
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7140102929
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