This study presents the development and evaluation of a ByteT5-based
multilingual translation model tailored for translating the Bible into
underrepresented languages. Utilizing the comprehensive Johns Hopkins
University Bible Corpus, we trained the model to capture the intricate nuances
of character-based and morphologically rich languages. Our results, measured by
the BLEU score and supplemented with sample translations, suggest the model can
improve accessibility to sacred texts. It effectively handles the distinctive
biblical lexicon and structure, thus bridging the linguistic divide. The study
also discusses the model's limitations and suggests pathways for future
enhancements, focusing on expanding access to sacred literature across
linguistic boundaries.

本研究提出了一种基于 ByteT5 的多语言翻译模型，旨在将圣经翻译成少数语言。通过使用约翰・霍普金斯大学圣经语料库进行训练，我们使模型能够捕捉基于字符和形态丰富的语言的复杂细微差别。我们的结果通过 BLEU 分数和样本翻译进行衡量，表明该模型可以提高对神圣文本的可访问性。它有效地处理了独特的圣经词汇和结构，从而弥合了语言分歧。本研究还讨论了模型的局限性，并提出了未来增强的途径，重点是在跨语言界限上扩大对神圣文学的获取。

ByteT5 在代表性较弱的语言的《圣经》文本多语言翻译中的效果

Efficacy of ByteT5 in Multilingual Translation of Biblical Texts for  Underrepresented Languages

In the prose style transfer task a system, provided with text input and a
target prose style, produces output which preserves the meaning of the input
text but alters the style. These systems require parallel data for evaluation
of results and usually make use of parallel data for training. Currently, there
are few publicly available corpora for this task. In this work, we identify a
high-quality source of aligned, stylistically distinct text in different
versions of the Bible. We provide a standardized split, into training,
development and testing data, of the public domain versions in our corpus. This
corpus is highly parallel since many Bible versions are included. Sentences are
aligned due to the presence of chapter and verse numbers within all versions of
the text. In addition to the corpus, we present the results, as measured by the
BLEU and PINC metrics, of several models trained on our data which can serve as
baselines for future research. While we present these data as a style transfer
corpus, we believe that it is of unmatched quality and may be useful for other
natural language tasks as well.

该论文通过语体转换任务，给定目标散文语体，提出了一个系统，生成样式改变但保留输入文本意义的输出，介绍了一种高质量的，对齐的，有样式差异的文本源，以圣经不同版本提供了标准化的训练，开发和测试数据集并报告了评价指标 BLEU 和 PINC, 本任务数据集无与伦比，可用于其他自然语言任务.