Although researchers and practitioners are pushing the boundaries and
enhancing the capacities of NLP tools and methods, works on African languages
are lagging. A lot of focus on well resourced languages such as English,
Japanese, German, French, Russian, Mandarin Chinese etc. Over 97% of the
world's 7000 languages, including African languages, are low resourced for NLP
i.e. they have little or no data, tools, and techniques for NLP research. For
instance, only 5 out of 2965, 0.19% authors of full text papers in the ACL
Anthology extracted from the 5 major conferences in 2018 ACL, NAACL, EMNLP,
COLING and CoNLL, are affiliated to African institutions. In this work, we
discuss our effort toward building a standard machine translation benchmark
dataset for Igbo, one of the 3 major Nigerian languages. Igbo is spoken by more
than 50 million people globally with over 50% of the speakers are in
southeastern Nigeria. Igbo is low resourced although there have been some
efforts toward developing IgboNLP such as part of speech tagging and diacritic
restoration

该研究论文介绍了关于自然语言处理在非洲语言的应用的研究进展，指出非洲语言的自然语言处理技术和工具存在落后的现象。在此背景下，作者提出了建立一个 Igbo 语言的机器翻译基准数据集的思路，从而提升非洲语言的自然语言技术水平。

伊博语 - 英语机器翻译：评估基准

Igbo-English Machine Translation: An Evaluation Benchmark

Unlike major Western languages, most African languages are very
low-resourced. Furthermore, the resources that do exist are often scattered and
difficult to obtain and discover. As a result, the data and code for existing
research has rarely been shared. This has lead a struggle to reproduce reported
results, and few publicly available benchmarks for African machine translation
models exist. To start to address these problems, we trained neural machine
translation models for 5 Southern African languages on publicly-available
datasets. Code is provided for training the models and evaluate the models on a
newly released evaluation set, with the aim of spur future research in the
field for Southern African languages.

该研究使用公共数据集为 5 种南非语言训练了神经机器翻译模型，并提供了训练和评估模型的代码，旨在促进南非语言领域的未来研究。