Extremely low-resource machine translation for closely related languages

2021/05/27 by Maali Tars, Tars, Maali, Andre Tättar +3 · 3 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2105.13065

openalex publication_date 2021/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

An effective method to improve extremely low-resource neural machine translation is multilingual training, which can be improved by leveraging monolingual data to create synthetic bilingual corpora using the back-translation method. This work focuses on closely related languages from the Uralic language family: from Estonian and Finnish geographical regions. We find that multilingual learning and synthetic corpora increase the translation quality in every language pair for which we have data. We show that transfer learning and fine-tuning are very effective for doing low-resource machine translation and achieve the best results. We collected new parallel data for Võro, North and South Saami and present first results of neural machine translation for these languages.

Citations

• Sockeye: A Toolkit for Neural Machine Translation
• Multi-Domain Neural Machine Translation
• Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation

Cited by

• Cross-Lingual Transfer for Machine Translation in Turkic Languages
• CycleDistill: Bootstrapping Machine Translation using LLMs with Cyclical Distillation
• Limited-Resource Adapters Are Regularizers, Not Linguists

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