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Neural machine translation (NMT) for low-resource local languages inIndonesia faces significant challenges, including the need for a representativebenchmark and limited data availability. This work addresses these challengesby comprehensively analyzing training NMT systems for four low-resource locallanguages in Indonesia: Javanese, Sundanese, Minangkabau, and Balinese. Ourstudy encompasses various training approaches, paradigms, data sizes, and apreliminary study into using large language models for synthetic low-resourcelanguages parallel data generation. We reveal specific trends and insights intopractical strategies for low-resource language translation. Our researchdemonstrates that despite limited computational resources and textual data,several of our NMT systems achieve competitive performances, rivaling thetranslation quality of zero-shot gpt-3.5-turbo. These findings significantlyadvance NMT for low-resource languages, offering valuable guidance forresearchers in similar contexts.

Replicable Benchmarking of Neural Machine Translation (NMT) on Low-Resource Local Languages in Indonesia