  This paper investigates the development and evaluation of machine translation
models from Cantonese to English, where we propose a novel approach to tackle
low-resource language translations. The main objectives of the study are to
develop a model that can effectively translate Cantonese to English and
evaluate it against state-of-the-art commercial models. To achieve this, a new
parallel corpus has been created by combining different available corpora
online with preprocessing and cleaning. In addition, a monolingual Cantonese
dataset has been created through web scraping to aid the synthetic parallel
corpus generation. Following the data collection process, several approaches,
including fine-tuning models, back-translation, and model switch, have been
used. The translation quality of models has been evaluated with multiple
quality metrics, including lexicon-based metrics (SacreBLEU and hLEPOR) and
embedding-space metrics (COMET and BERTscore). Based on the automatic metrics,
the best model is selected and compared against the 2 best commercial
translators using the human evaluation framework HOPES. The best model proposed
in this investigation (NLLB-mBART) with model switch mechanisms has reached
comparable and even better automatic evaluation scores against State-of-the-art
commercial models (Bing and Baidu Translators), with a SacreBLEU score of 16.8
on our test set. Furthermore, an open-source web application has been developed
to allow users to translate between Cantonese and English, with the different
trained models available for effective comparisons between models from this
investigation and users. CANTONMT is available at
https://github.com/kenrickkung/CantoneseTranslation
