  Machine translation focuses mainly on high-resource languages (HRLs), while
low-resource languages (LRLs) like Taiwanese Hokkien are relatively
under-explored. The study aims to address this gap by developing a dual
translation model between Taiwanese Hokkien and both Traditional Mandarin
Chinese and English. We employ a pre-trained LLaMA 2-7B model specialized in
Traditional Mandarin Chinese to leverage the orthographic similarities between
Taiwanese Hokkien Han and Traditional Mandarin Chinese. Our comprehensive
experiments involve translation tasks across various writing systems of
Taiwanese Hokkien as well as between Taiwanese Hokkien and other HRLs. We find
that the use of a limited monolingual corpus still further improves the model's
Taiwanese Hokkien capabilities. We then utilize our translation model to
standardize all Taiwanese Hokkien writing systems into Hokkien Han, resulting
in further performance improvements. Additionally, we introduce an evaluation
method incorporating back-translation and GPT-4 to ensure reliable translation
quality assessment even for LRLs. The study contributes to narrowing the
resource gap for Taiwanese Hokkien and empirically investigates the advantages
and limitations of pre-training and fine-tuning based on LLaMA 2.
