  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

Computation and Language;Machine Learning