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Igbo-English Machine Translation: An Evaluation Benchmark

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

Publication date1/04/2020
Number of pages4
<mark>Original language</mark>English
EventEighth International Conference on Learning Representations: ICLR 2020 - Virtual
Duration: 26/04/202030/04/2020
Conference number: 8th


ConferenceEighth International Conference on Learning Representations
Internet address


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