Not All Data Augmentation Works: A Typology-Aware Study for Low-Resource Neural Machine Translation in Vietnamese Ethnic Minority Languages

Long Nguyen, Dat T. Truong, Nhan D. Tran, Quynh Vo, Quy Tran Nguyen, Tho Quan

Keywords: Low-Resource NMT, Data Augmentation, Typology-Aware NLP, Vietnamese Ethnic Minority Languages, Morphology-Aware Augmentation, Underserved Languages

TL;DR: We show that not all data augmentation methods are helpful; only linguistically compatible, meaning-preserving strategies improve low-resource NMT for Tày and Bahnar, two typologically distinct Vietnamese minority languages.

Abstract: Neural Machine Translation (NMT) for low-resource and underserved languages remains challenging due to the severe lack of parallel corpora, linguistic tools, and evaluation resources. The issue is evident in Vietnam, where the ethnolinguistic minority languages Tày (Tai–Kadai) and Bahnar (Austroasiatic) hold cultural significance but remain digitally under-represented. Data Augmentation (DA) offers a cost-effective remedy; however, most existing techniques were designed for high-resource analytic languages and are often applied heuristically without assessing their linguistic compatibility. In this work, we present the first systematic study of DA for two minority language pairs, Tày–Vietnamese and Bahnar–Vietnamese, within a three-stage language model pipeline consisting of Vietnamese-based initialization, monolingual adaptation, and supervised fine-tuning. We train two independent encoder–decoder NMT systems to isolate augmentation effects and analyze how linguistic typology shapes augmentation behavior. Our experiments show that meaning-preserving DA methods consistently improve translation adequacy and linguistic faithfulness, whereas several widely used techniques introduce semantic or structural degradation. Through quantitative evaluation and typology-aware linguistic analysis, we derive practical guidelines for selecting DA strategies in extremely low-resource and typologically diverse settings. We additionally release newly digitized high-quality bilingual corpora and trained models to facilitate future research and community-centered NLP development.

Submission Number: 41