CANTONMT: Cantonese to English NMT Platform with Fine-Tuned Models using Synthetic Back-Translation Data

Core Concepts

Investigating back-translation for synthetic data generation in Cantonese-to-English NMT.

Abstract

The study focuses on developing a Cantonese-to-English Neural Machine Translation (NMT) platform using back-translation for synthetic data augmentation. It addresses the challenges faced by low-resource languages in NLP research, particularly focusing on Cantonese translation. The research explores various models like OpusMT, NLLB, and mBART fine-tuned with real and synthetic data. The study aims to facilitate research in Cantonese-English translation through the CANTONMT project and an open-source toolkit. It compares different models, methodologies, and evaluation metrics to enhance machine translation performance.

Stats

Population of Guangdong province: 126.84 million in 2021. Population of Hong Kong: 7,503,100 in 2023. Population of Macau: 704,149 in 2023.

Quotes

"Data augmentation via Backtranslation has been one of the standard practices to generate a synthetic corpus for assisting the MT performances of low-resource language pairs." "In this work, we aim at investigating one of the popular methods, i.e. synthetic data augmentation via back-translation and model fine-tuning, on Cantonese-to-English neural MT (NMT), a new language pair." "The experiments show that all the fine-tuned models outperformed the baseline deployment models with large margins."

Key Insights Distilled From

CantonMT

by Kung Yin Hon... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11346.pdf

Deeper Inquiries

How can the CANTONMT platform address privacy concerns related to sensitive data?

The CANTONMT platform addresses privacy concerns related to sensitive data by being open-source, allowing users full control over the data they input and output. This means that researchers can fine-tune models with their own data, ensuring confidentiality and security. Unlike commercial translation engines where there may be risks to data privacy, CANTONMT provides a safe environment for users to work with sensitive information without third-party interference.

What are the implications of using synthetic data augmentation for improving machine translation performance?

Using synthetic data augmentation has significant implications for enhancing machine translation performance. By generating additional training examples through back-translation methods, NLP researchers can increase the amount of available training data, especially in low-resource language pairs like Cantonese-to-English. This process helps improve model generalization and robustness by exposing it to more diverse linguistic patterns present in the synthetic corpus. Ultimately, this leads to better translation quality and accuracy when deploying these fine-tuned models.

How does the availability of more real data impact model performances in NMT systems?

The availability of more real data positively impacts model performances in Neural Machine Translation (NMT) systems. In the case of CANTONMT, acquiring an additional 14.5K parallel Cantonese-English dictionary corpus from Wenlin.com led to improved scores across various evaluation metrics compared to models trained solely on a smaller dataset from words.hk. The increased volume of real-world bilingual text allows models like NLLB-200 and mBART to learn more nuanced language patterns and nuances, resulting in enhanced translation capabilities such as higher accuracy and fluency levels. This demonstrates that high-quality real training datasets play a crucial role in boosting NMT system effectiveness.