  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.
