This paper investigates the challenges of low-resource machine translation for ʻŌlelo Hawaiʻi (Hawaiian), a critically endangered Polynesian language. We compile a corpus of publicly available Hawaiian-English bitext and investigate the effectiveness of neural sequence-to-sequence models and large language models for translating Hawaiian. To address data scarcity, we employ various data augmentation techniques, including backtranslation, multilingual training using parallel corpora in related languages, and leveraging dictionary entries. Our experiments demonstrate that multilingual training significantly improves model performance, particularly when incorporating bitext from related Polynesian languages. Fine-tuned large language models were not able to outperform mBART, highlighting that smaller and simpler models are still relevant, especially in low-resource scenarios.