Efficiently and accurately translating a corpus into a low-resource language
remains a challenge, regardless of the strategies employed, whether manual,
automated, or a combination of the two. Many Christian organizations are
dedicated to the task of translating the Holy Bible into languages that lack a
modern translation. Bible translation (BT) work is currently underway for over
3000 extremely low resource languages. We introduce the eBible corpus: a
dataset containing 1009 translations of portions of the Bible with data in 833
different languages across 75 language families. In addition to a BT
benchmarking dataset, we introduce model performance benchmarks built on the No
Language Left Behind (NLLB) neural machine translation (NMT) models. Finally,
we describe several problems specific to the domain of BT and consider how the
established data and model benchmarks might be used for future translation
efforts. For a BT task trained with NLLB, Austronesian and Trans-New Guinea
language families achieve 35.1 and 31.6 BLEU scores respectively, which spurs
future innovations for NMT for low-resource languages in Papua New Guinea.

使用 eBible 语料库作为基准数据集，引入神经机器翻译模型性能基准，并考虑了多种与圣经翻译领域相关的问题，最终建立了一个适用于低资源语言的模型基准。

eBible 语料库：为低资源语言的圣经翻译提供数据和模型基准

The eBible Corpus: Data and Model Benchmarks for Bible Translation for  Low-Resource Languages

Technology has increasingly become an integral part of the Bible translation
process. Over time, both the translation process and relevant technology have
evolved greatly. More recently, the field of Natural Language Processing (NLP)
has made great progress in solving some problems previously thought
impenetrable. Through this study we endeavor to better understand and
communicate about a segment of the current landscape of the Bible translation
process as it relates to technology and identify pertinent issues. We conduct
several interviews with individuals working in different levels of the Bible
translation process from multiple organizations to identify gaps and
bottlenecks where technology (including recent advances in AI) could
potentially play a pivotal role in reducing translation time and improving
overall quality.

本研究通过对多个组织的不同层面的圣经翻译过程中存在的瓶颈和问题进行访谈，探究在现今自然语言处理技术和人工智能的新进展下，技术能够如何缩短翻译时间、提高翻译质量的作用。