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Igbo-English Machine Translation: An Evaluation Benchmark

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

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Igbo-English Machine Translation : An Evaluation Benchmark. / Ezeani, Ignatius; Rayson, Paul; Onyenwe, Ikechukwu E.; Chinedu, Uchechukwu; Hepple, Mark.

2020. Paper presented at Eighth International Conference on Learning Representations, .

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

Harvard

Ezeani, I, Rayson, P, Onyenwe, IE, Chinedu, U & Hepple, M 2020, 'Igbo-English Machine Translation: An Evaluation Benchmark', Paper presented at Eighth International Conference on Learning Representations, 26/04/20 - 30/04/20.

APA

Ezeani, I., Rayson, P., Onyenwe, I. E., Chinedu, U., & Hepple, M. (2020). Igbo-English Machine Translation: An Evaluation Benchmark. Paper presented at Eighth International Conference on Learning Representations, .

Vancouver

Ezeani I, Rayson P, Onyenwe IE, Chinedu U, Hepple M. Igbo-English Machine Translation: An Evaluation Benchmark. 2020. Paper presented at Eighth International Conference on Learning Representations, .

Author

Ezeani, Ignatius ; Rayson, Paul ; Onyenwe, Ikechukwu E. ; Chinedu, Uchechukwu ; Hepple, Mark. / Igbo-English Machine Translation : An Evaluation Benchmark. Paper presented at Eighth International Conference on Learning Representations, .4 p.

Bibtex

@conference{58826031b4ac4806992d53c77bdb4564,
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",
author = "Ignatius Ezeani and Paul Rayson and Onyenwe, {Ikechukwu E.} and Uchechukwu Chinedu and Mark Hepple",
year = "2020",
month = apr
day = "1",
language = "English",
note = "Eighth International Conference on Learning Representations : ICLR 2020 ; Conference date: 26-04-2020 Through 30-04-2020",
url = "http://ilcr.cc",

}

RIS

TY - CONF

T1 - Igbo-English Machine Translation

T2 - Eighth International Conference on Learning Representations

AU - Ezeani, Ignatius

AU - Rayson, Paul

AU - Onyenwe, Ikechukwu E.

AU - Chinedu, Uchechukwu

AU - Hepple, Mark

N1 - Conference code: 8th

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

M3 - Conference paper

Y2 - 26 April 2020 through 30 April 2020

ER -