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Enhancing Hokkien Dual Translation by Standardizing Writing Systems


Core Concepts
Developing a dual translation model for Taiwanese Hokkien bridges the resource gap, emphasizing the importance of monolingual corpora and standardization.
Abstract
The study focuses on developing a dual translation model for Taiwanese Hokkien to address the challenges faced by low-resource languages. By leveraging pre-trained models specialized in Mandarin Chinese, the research aims to improve translation tasks across various writing systems of Taiwanese Hokkien and other high-resource languages. The experiments conducted show that utilizing monolingual corpora enhances the model's capabilities in translating between different writing systems of Taiwanese Hokkien and other languages. Standardizing all Taiwanese Hokkien writing systems into Hokkien Han further improves performance. The study also introduces an evaluation method incorporating back-translation and GPT-4 for reliable quality assessment, contributing to narrowing the resource gap for Taiwanese Hokkien.
Stats
Machine translation focuses mainly on high-resource languages (HRLs). Employed a pre-trained LLaMA2-7B model specialized in Traditional Mandarin Chinese. Use of limited monolingual corpus improves the model’s capabilities. Incorporating parallel datasets involving HRL improves performance. Standardizing all Taiwanese Hokkien writing systems into Hokkien Han slightly improves translation performance.
Quotes
"We employ a pre-trained LLaMA 2 model specialized in Mandarin Chinese to develop a translation model capable of translating between different writing systems of Taiwanese Hokkien." "Our findings indicate that using a monolingual corpus covering all Taiwanese Hokkien writing systems positively impacts the model’s dual translation performance." "The study contributes to narrowing the resource gap for Taiwanese Hokkien and empirically investigates the advantages and limitations of pre-training and fine-tuning based on LLaMA 2."

Deeper Inquiries

How can this research be extended to include other prevalent spoken languages in Taiwan?

This research can be extended to include other prevalent spoken languages in Taiwan by following a similar methodology of leveraging large language models pre-trained on high-resource languages that share similarities with the target low-resource language. By collecting diverse datasets from different linguistic sources, such as news articles, literary texts, and educational materials, researchers can build translation models for these languages. Additionally, incorporating parallel data from related high-resource languages and fine-tuning the model on specific language pairs can help improve translation capabilities for multiple languages simultaneously.

What are potential biases that may arise from utilizing skewed data sources, and how can they be mitigated?

Utilizing skewed data sources in training large language models for translation tasks may introduce biases into the model's outputs. Biases could stem from ideological stances present in news articles or political inclinations reflected in certain texts used for training. To mitigate these biases, researchers should aim to diversify their dataset by including neutral literary texts, academic papers, and culturally representative materials. Furthermore, implementing bias detection algorithms during model training and post-training evaluation can help identify and address any biased patterns present in the translations generated by the model.

How does standardizing writing systems impact translation quality beyond just improving performance?

Standardizing writing systems not only improves performance but also enhances translation quality by ensuring consistency across different scripts or orthographies. When all monolingual corpora are standardized into a single writing system before continued pre-training of the translation model, it helps align vocabulary usage and grammatical structures more effectively. This alignment leads to more accurate translations between different writing systems within the same language while reducing ambiguity or errors caused by variations in script representation. Standardization also aids in creating a unified reference point for evaluating translations across various scripts or dialects within a given language context.
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