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

Kung Yin Hong, Lifeng Han, Riza Batista-Navarro, Goran Nenadic

TL;DR

This work deploys a standard data augmentation methodology by back-translation to a new language translation direction, i.e., Cantonese-to-English, and creates a user-friendly interface for the models included in this project, CantonMT, and makes it available to facilitate Cantonese-to-English MT research.

Abstract

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}.

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

TL;DR

This work deploys a standard data augmentation methodology by back-translation to a new language translation direction, i.e., Cantonese-to-English, and creates a user-friendly interface for the models included in this project, CantonMT, and makes it available to facilitate Cantonese-to-English MT research.

Abstract

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}.
Paper Structure (11 sections, 5 figures, 4 tables)

This paper contains 11 sections, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Experimental Work
  4. Methodology and Framework
  5. Evaluations of CantonMT
  6. Adding More Real Data
  7. CantonMT Platform
  8. User Interface
  9. Server
  10. Discussion and Conclusion
  11. Appendix

Figures (5)

  • Figure 1: CantonMT Pipeline: data collection and preprocessing, synthetic data generation, model fine-tuning, model evaluation
  • Figure 2: Learning curves during model training using real data.
  • Figure 3: CantonMT Server and Interface Flowchart diagram.
  • Figure 4: CantonMT Platform with options of model types, training categories, and translating directions. Frontend: TypeScript with Next.js. Backend: Python - Flask
  • Figure 5: Example text extracted from LIHKG website with lots noise before cleaning and anonymisation