CantonMT : C antonese to E nglish NMT Platform with Fine-Tuned Models using Real and Synthetic Back-Translation Data (2024)

Abstract: Neural Machine Translation (NMT) for low-resource languages remains a challenge for many NLP researchers. In this work, we deploy a standard data augmentation methodology by back-translation to a new language translation direction, i.e., 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 by three models: OpusMT, NLLB, and mBART.We carried out automatic evaluation using a range of different metrics including those that are lexical-based and embedding-based.Furthermore, we create a user-friendly interface for the models we included in this project, CantonMT, and make it available to facilitate Cantonese-to-English MT research. Researchers can add more models to this platform via our open-source CantonMT toolkit, available at https://github.com/kenrickkung/CantoneseTranslation .

## introduction cantonese is one of the most popular dialects of chinese languages, after the standard language mandarin (the current official language in china, originally from the beijing area), originally from the capital of guangdong province, guangzhou (a.k.a. canton) in china. the population of guangdong province was 129.51 million in 2022 according to the national bureau of statistics of china foot_0 . in addition, cantonese is also the native language in hong kong (hk) and macau regions which have populations of 7,503,100 and 704,149 in 2023, according to hk census and statistics department foot_1 and macrotrends global population statistics. 3 furthermore, because of the economic growth in guangdong, hk and macau, many people from other chinese provinces also learned to speak cantonese for job purposes and due to cultural influences. there is also a large global population outside of china speaking cantonese, 85.5 million, according to the cantonese language association (cla) 4 . in the era of the fast development of natural language processing (nlp), many machine translation (mt) models have been proposed for the majority of languages worldwide. however, low-resource language mt remains a challenge for researchers. cantonese translation using mt, specifically, is under-explored and has not been given much attention thus far. in this work, we investigate one of the more popular mt methods, i.e. synthetic data augmentation via back-translation and model fine-tuning, as an approach to cantonese-to-english neural mt (nmt), along the way introducing cantonese-english as a new language pair. we select several models for evaluation including both smaller and larger language models, and compare their system performance using a range of evaluation metrics. furthermore, we open-source our toolkit and create a webbased user-friendly platform called cantonmt to facilitate research on cantonese-english translation. a public video demo is available. 5 in the next section (section 2), we survey related work on cantonese-english mt, data augmentation for mt, and available demos/engines. section 3 introduces our methodology and framework. section 4 explains the web-based cantonmt platform. section 5 concludes this work with a discussion. ## related work research work focussing on cantonese-english mt has not gained much attention to date. earliest efforts include the work of @xcite where example-based and rule-based mt were investigated. in recent years, a project plan on cantonese-english translation was put forward by researchers at the university of hong kong (hku) where they proposed to investigate various mt approaches, including rule-based mt (rbmt), example-based mt (ebmt), statistical mt (smt), gated-recurrent units (gru) and transformers @xcite . more loosely related work include research in mt for cantonese, but without english as the target language. these include dialectal translation between cantonese and mandarin chinese by @xcite , yi @xcite and liu @xcite . data augmentation via backtranslation has been one of the standard practices for generating a synthetic corpus for improving mt performance on low-resource language pairs. this has been popular for both statistical mt (smt) and nmt @xcite @xcite @xcite . however, to the best of our knowledge, none of these efforts focused on cantonese-to-english translation. existing platforms or off-the-shelf demos for cantonese-to-english mt are very scarce. popular mt engines from commercial it companies, including google translate foot_5 and deepl translator, foot_6 do not include this language pair. both of them only included simplified and traditional characters of mandarin chinese. meanwhile, microsoft bing translator foot_7 and baidu, an it company from china, made the baidu translator (fanyi) foot_8 available, which includes cantonese among several chinese dialectal languages. 10 in the opposite direction, there are open-source tools for english-to-cantonese mt from transcan. foot_10 ## experimental work we introduce the methodology of cantonmt, experimental evaluations using the initial 38k real bilingual corpus, and extended model evaluations when we acquired 14.5k and 10k more real bilingual data from different sources subsequently. ## cantonmt platform to further facilitate cantonese-english mt research and for users to easily access freely available fine-tuned models, we developed a user-friendly interface for the cantonmt platform. users can choose different models and translation directions (cantonese⇔english) via the interface (figure 4 in the appendix). the web application contains two main parts, the interface and the server. model manager next.js mbart model loader opus-mt model loader nllb model loader flask server interface 1) user input sentence 2) send api request 3) load model (if needed) & send sentence to model loader 4) model outputs translated sentence 5) send json ## discussion and conclusion in this work, we investigated the back-translation methodology for bilingual synthetic data generation for the sake of data augmentation for nmt, on a new language translation direction, cantonese-to-english. we tested both smaller-sized opusmt and extra-large llms nllb and mbart both using available bilingual real data and larger synthetic data. our experiments show that all the fine-tuned models outperformed the baseline deployment models with large margins. furthermore, the synthetic model nllb-syn-1:1-mbart produced higher scores using the model switch method compared to those without the model switch. lastly, the best performing fine-tuned models have similar (or even higher) evaluation scores than the current commercially available translators of baidu and microsoft-bing. in terms of concerns of data privacy such as handling of sensitive data (e.g., in clinical applications related to health analytics of patient data @xcite ), cantonmt can be fully controlled by users without interference from any third parties. we open-source our platform so that researchers can continue to integrate new models into the toolkit to promote cantonese-english mt. we also plan to carry out human evaluations on the outputs from different systems to get more insights into the system errors.