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Language Revitalization: A Benchmark for Akan-to-English Machine Translation

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Intelligent Systems and Applications (IntelliSys 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1251))

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Abstract

Language reconciles the ideas, beliefs and values of people from diverse cultural, social, economic, religious and professional backgrounds, and it is imperative for sustainable development. Undoubtedly, the emergence of Neural Machine Translation has gained significant advancement in language translation automation, and consequently, establishing seamless reconciliation between two communicating entities with diverse backgrounds. Comprising of sequence to sequence architecture, Neural Machine Translation models have outperformed statistical and rule-based models in terms of concordance and syntax, regardless of how complex the language structure is. However, structured language resources are scarce for low-resource languages like Akan. Research contributions toward the revitalization of these languages are limited, along with subtle adverse events such as cultural assimilation and language imperialism. In order to solve the problem of low-resource machine translation from Akan to English, we mine and use the first parallel corpus for the Akan-English translation. We establish a benchmark for the Akan-English translation using a deep hierarchical end-to-end attention-based neural machine translation model trained on the parallel corpus. Experimental results further confirm the effectiveness of our approach towards the task. Nevertheless, a confirmed grammatical parity between the two languages bequests improvement approaching a substantial revitalization of the Akan language.

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Notes

  1. 1.

    https://github.com/KingsleyNA/Parallel-Data-For-The-Revitalization-of-Tw-or-The-Akan-Language.

  2. 2.

    https://scrapy.org.

  3. 3.

    https://www.bible.com/versions/1461-aswdc-twer-kronkron.

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Author information

Authors and Affiliations

  1. School of Information and Software Engineering (SISE), University of Electronic Science and Technology of China (UESTC), Chengdu, 610054, Sichuan, China

    Kingsley Nketia Acheampong & Nathaniel Nii Oku Sackey

  2. Information Intelligence Technology Lab, SISE-UESTC, Chengdu, China

    Kingsley Nketia Acheampong

Authors
  1. Kingsley Nketia Acheampong
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  2. Nathaniel Nii Oku Sackey
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Corresponding author

Correspondence to Kingsley Nketia Acheampong .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Acheampong, K.N., Sackey, N.N.O. (2021). Language Revitalization: A Benchmark for Akan-to-English Machine Translation. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1251. Springer, Cham. https://doi.org/10.1007/978-3-030-55187-2_20

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