ChavacanoMNMT: Exploring Language Influences in the Multilingual Neural Machine Translation of a Spanish-Based Creole

Multilingual Neural Machine Translation (MNMT) enhances low-resource language translation by leveraging related languages, yet its application to Creole languages like Chavacano, a Spanish-based Creole spoken in the Philippines, remains underexplored. This study investigates the influence of Austronesian (Cebuano, Hiligaynon) and Indo-European (Spanish, English) parent languages on Chavacano MNMT performance. We fine-tuned an mT5-based model on the ChavacanoMT dataset, which comprises 1,044,185 samples spanning 20 language pairs involving Chavacano and related languages. Ablation experiments were conducted by excluding individual languages from training to evaluate their impact on model performance. Performance was assessed using BLEU, chrF++, and BERTScore metrics, with Paired Bootstrap Resampling applied to determine statistical significance. This study reports that Austronesian languages significantly influence Chavacano translation quality more than Indo-European languages (5 BLEU points, p=0.001). Cebuano and Hiligaynon enhance translation quality (p ≤ 0.025), while Spanish contributes less due to structural divergence (VSO vs. SVO) despite Chavacano’s 77% lexical similarity with Spanish. Our model achieves a 17 BLEU point improvement in Chavacano-English translation over prior benchmarks. These findings highlight the role of structural similarity in MNMT and inform auxiliary language selection for low-resource Creole translation, contributing new benchmarks and insights for Chavacano NLP.