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Igbo-English Machine Translation: An Evaluation Benchmark

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Igbo-English Machine Translation: An Evaluation Benchmark. / Ezeani, Ignatius; Rayson, Paul; Onyenwe, Ikechukwu et al.
In: arXiv, 01.04.2020.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Ezeani, I, Rayson, P, Onyenwe, I, Uchechukwu, C & Hepple, M 2020, 'Igbo-English Machine Translation: An Evaluation Benchmark', arXiv. <https://arxiv.org/abs/2004.00648>

APA

Ezeani, I., Rayson, P., Onyenwe, I., Uchechukwu, C., & Hepple, M. (2020). Igbo-English Machine Translation: An Evaluation Benchmark. arXiv. https://arxiv.org/abs/2004.00648

Vancouver

Ezeani I, Rayson P, Onyenwe I, Uchechukwu C, Hepple M. Igbo-English Machine Translation: An Evaluation Benchmark. arXiv. 2020 Apr 1.

Author

Ezeani, Ignatius ; Rayson, Paul ; Onyenwe, Ikechukwu et al. / Igbo-English Machine Translation : An Evaluation Benchmark. In: arXiv. 2020.

Bibtex

@article{2bb3f1568276450b8a12d77f6281fbe4,
title = "Igbo-English Machine Translation: An Evaluation Benchmark",
abstract = "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 ",
keywords = "cs.CL, cs.LG",
author = "Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple",
note = "4 pages",
year = "2020",
month = apr,
day = "1",
language = "English",
journal = "arXiv",

}

RIS

TY - JOUR

T1 - Igbo-English Machine Translation

T2 - An Evaluation Benchmark

AU - Ezeani, Ignatius

AU - Rayson, Paul

AU - Onyenwe, Ikechukwu

AU - Uchechukwu, Chinedu

AU - Hepple, Mark

N1 - 4 pages

PY - 2020/4/1

Y1 - 2020/4/1

N2 - 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

AB - 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

KW - cs.CL

KW - cs.LG

M3 - Journal article

JO - arXiv

JF - arXiv

ER -