Data Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Q'eqchi' Mayan
arXiv:2606.09767v1 Announce Type: cross Abstract: Neural machine translation for digitally low-resource Indigenous languages is often hindered by extreme data scarcity, prompting reliance on extractive web-scraping. To ensure data sovereignty, this study introduces a data synthesis methodology to bootstrap NMT models without scraping target-language parallel text. Focusing on Q'eqchi' Mayan, we transformed community-sourced dictionaries into a massive synthetic corpus, utilizing Parameter-Efficient Fine-Tuning (PEFT) via LoRA adapters on an mT5-base model. In-domain evaluation demonstrates hig
The increasing focus on AI development for diverse languages and the ethical implications of data collection are driving innovation in synthetic data generation methods to preserve data sovereignty.
This study demonstrates a viable solution for bootstrapping NMT models for low-resource Indigenous languages without relying on extractive web-scraping, ensuring data sovereignty and ethical AI development.
The ability to create high-quality synthetic data from community-sourced dictionaries offers a new paradigm for NMT development, particularly for languages previously lacking sufficient digital resources.
- · Indigenous language communities
- · NLP researchers
- · Ethical AI developers
- · Generative AI platforms
- · Traditional data scraping methods
- · Monolingual AI ecosystems
More accurate and culturally relevant AI tools become available for a broader range of low-resource languages.
Increased digital preservation and revitalization efforts for Indigenous languages through the development of advanced language technologies.
The development of sovereign AI solutions for various regions and cultures, reducing dependency on a few dominant linguistic AI models.
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