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

• URL: http://arxiv.org/abs/2304.09919v1
• Date: Wed, 19 Apr 2023 18:52:49 GMT
• Title: The eBible Corpus: Data and Model Benchmarks for Bible Translation for Low-Resource Languages
• Authors: Vesa Akerman and David Baines and Damien Daspit and Ulf Hermjakob and Taeho Jang and Colin Leong and Michael Martin and Joel Mathew and Jonathan Robie and Marcus Schwarting
• Abstract summary: 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 dataset benchmarking, we introduce model performance benchmarks built on the No Language Left Behind (NLLB) neural machine translation (NMT) models.
• Score: 1.4681482563848867
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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