Enhancing Hokkien Dual Translation by Exploring and Standardizing of
  Four Writing Systems
Bo-Han Lu Yi-Hsuan Lin En-Shiun Annie Lee Richard Tzong-Han Tsai
Machine translation focuses mainly on high-resource languages (HRLs), while
low-resource languages (LRLs) like Taiwanese Hokkien are relatively
under-explored. This 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 LLaMA2-7B 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 and between Taiwanese Hokkien and other HRLs. We find that
the use of a limited monolingual corpus also further improve 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.