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English-Afaan Oromo Machine Translation Using Deep Attention Neural Network

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Abstract

Attention-based neural machine translation (attentional NMT), which jointly aligns and translates, has got much popularity in recent years. Besides, a language model needs an accurate and larger bilingual dataset_ from the source to the target, to boost translation performance. There are many such datasets publicly available for well-developed languages for model training. However, currently, there is no such dataset available for the English-Afaan Oromo pair to build NMT language models. To alleviate this problem, we manually prepared a 25K English-Afaan Oromo new dataset for our model. Language experts evaluate the prepared corpus for translation accuracy. We also used the publicly available English-French, and English-German datasets to see the translation performances among the three pairs. Further, we propose a deep attentional NMT model to train our models. Experimental results over the three language pairs demonstrate that the proposed system and our new dataset yield a significant gain. The result from the English-Afaan Oromo model achieved 1.19 BLEU points over the previous English-Afaan Oromo Machine Translation (MT) models. The result also indicated that the model could perform as closely as the other developed language pairs if supplied with a larger dataset. Our 25K new dataset also set a baseline for future researchers who have curiosity about English-Afaan Oromo machine translation.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sector.

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  1. Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, 600062, Chennai, TN, India

    Ebisa A. Gemechu & G. R. Kanagachidambaresan

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  1. Ebisa A. Gemechu
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  2. G. R. Kanagachidambaresan
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Correspondence to Ebisa A. Gemechu or G. R. Kanagachidambaresan.

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Ebisa A. Gemechu, Kanagachidambaresan, G.R. English-Afaan Oromo Machine Translation Using Deep Attention Neural Network. Opt. Mem. Neural Networks 32, 159–168 (2023). https://doi.org/10.3103/S1060992X23030049

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