Prompt-Based Translation of Chinese into Taiwanese Mandarin Braille.

In automated Braille translation, accommodating linguistic nuances and the rules peculiar to Braille across various languages poses considerable challenges. Mandarin Chinese stands out in this aspect due to its necessity to ascertain the appropriate pronunciation of characters based on context. Although rule-based algorithms have historically dominated this space, recent empirical evidence highlights the efficacy of statistical approaches and the emergent exploration of Large Language Model (LLM)-based techniques. This paper explores the potential advantages of leveraging a prompt-based strategy for the automated translation from Mandarin Chinese to Taiwanese Mandarin Braille. As a methodology, we devised a script capable of ingesting a Chinese sentence and subsequently generating a prompt that comprises the Zhuyin of unequivocal characters and dictionary definitions for those with polysemous readings. Utilizing a set of 103 test sentences, we assessed the precision with which GPT-3.5, GPT-4, and Liblouis (a widely-recognized open-source rule-based Braille translator) ascribed readings to polyphonic characters. Our findings revealed that, notwithstanding certain inconsistencies in the GPT-3.5 outputs, the extended GPT- 4 model exhibited superior performance compared to Liblouis.