# CantonMT: Cantonese to English NMT Platform with Fine-Tuned Models Using Synthetic Back-Translation Data

arXiv: https://arxiv.org/abs/2403.11346 · HTML: https://mlsanity.com/paper/2403.11346/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 17 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [On The Evaluation of Machine Translation Systems Trained With Back-Translation](https://mlsanity.com/paper/1908.05204/) (2019)
- [Generalizing Back-Translation in Neural Machine Translation](https://mlsanity.com/paper/1906.07286/) (2019)
- [cushLEPOR: customising hLEPOR metric using Optuna for higher agreement with human judgments or pre-trained language model LaBSE](https://mlsanity.com/paper/2108.09484/) (2021)
- [Cross-model Back-translated Distillation for Unsupervised Machine Translation](https://mlsanity.com/paper/2006.02163/) (2020)
- [Examining Large Pre-Trained Language Models for Machine Translation: What You Don't Know About It](https://mlsanity.com/paper/2209.07417/) (2022)

## Built on by (most cited first, 1 of 1)

- [SiniticMTError: A Machine Translation Dataset with Error Annotations for Sinitic Languages](https://mlsanity.com/paper/2509.20557/) (2025)

## Abstract (arXiv)

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 thistextsc 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-sourcetextsc CantonMT toolkit urlhttps://github.com/kenrickkung/CantoneseTranslation.