CantonMT: Cantonese to English NMT Platform with Fine-Tuned Models Using Synthetic Back-Translation Data
<|reference_start|>CantonMT: Cantonese to English NMT Platform with Fine-Tuned Models Using Synthetic Back-Translation Data: Neural Machine Translation (NMT) for low-resource languages is still a challenging task in front of NLP researchers. In this work, we deploy a standard data augmentation methodology by back-translation to a new language translation direction Cantonese-to-English. We present the models we fine-tuned using the limited amount of real data and the synthetic data we generated using back-translation including OpusMT, NLLB, and mBART. We carried out automatic evaluation using a range of different metrics including lexical-based and embedding-based. Furthermore. we create a user-friendly interface for the models we included in this\textsc{ CantonMT} research project and make it available to facilitate Cantonese-to-English MT research. Researchers can add more models into this platform via our open-source\textsc{ CantonMT} toolkit \url{https://github.com/kenrickkung/CantoneseTranslation}.<|reference_end|>
@article{hong2024cantonmt:,
    title={CantonMT: Cantonese to English NMT Platform with Fine-Tuned Models Using
  Synthetic Back-Translation Data},
    author={Kung Yin Hong, Lifeng Han, Riza Batista-Navarro, Goran Nenadic},
    journal={arXiv preprint arXiv:2403.11346},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11346},
    primaryClass={cs.CL cs.AI}
}