Digitalizing Maguindanaon Language Using Corpus-Based Approach and Statistical Machine Translation (Smt)

This paper focuses on Maguindanaon, a language spoken by the Maguindanaon people in the Philippines, particularly in the provinces of Maguindanao del Norte and del Sur. With about two million speakers, the language has significant cultural value but is facing a decline as younger generations shift to more widely spoken languages. Unfortunately, there are limited resources and digital tools available to support the language, putting its future at risk. To address this, this paper develops a small-scale Maguindanaon-English bilingual corpus and uses it to test Statistical Machine Translation (SMT) models, including IBM Models 1 and 2 and a Phrase-based model. The goal was to explore the potential for using machine translation to bridge the gap between Maguindanaon and English. Although the results showed that all models struggled due to the small size of the corpus, with the Phrase-based model achieving the highest but still low BLEU scores, the study highlights the potential of machine translation for under-resourced languages. The findings emphasize the need for a larger, better-aligned corpus and improved techniques to enhance translation quality, laying the groundwork for future efforts in both language preservation and machine translation for Maguindanaon.