Year: 2026
Type: conference-paper
Abstract: We describe our submission to the shared task LoResMT 2026, which involved translating from low-resource Turkic languages Bashkir, Chuvash, Kazakh, Kyrgyz, and Tatar from English or Russian.We submitted runs for the English-Chuvash language pair using Neural machine translation (NMT).Our approach focused on systematic experimentation with diverse model architectures and an emphasis on optimizing inference-time parameters.The key findings indicate that a large-scale, specialized multilingual translation model, combined with targeted data preprocessing and careful generation tuning, yielded the best performance, achieving a chrF++ score of 29.67 on the public test set.
Author: Vladimir Panov
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7140140793
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