A Hybrid Translation Model for Pidgin English to English Language Translation
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Description
The African continent is made up of people with rich diverse cultures and
spoken languages. Despite the diversity, one common point of unification,
especially among the West African communities is the spoken pidgin-English
language. With the development in web technology and the English language
dominancy of web content, this growing population stands disadvantaged in
understanding content on the web. To proffer a solution, researchers in
machine translation from Pidgin English to the English language have
leveraged only unsupervised and supervised Neural Machine Translationbased
models. In this paper, we propose a hybrid-strategic model that
improves the accuracy of the baseline Neural Machine Translation Model
(NMT) in translating pidgin English to the English language. From the JW300
public dataset, we used 22,047 sentence pairs for training our model,1000 for tuning, and 2520 for testing. The Bi-Lingual Evaluation Understudy (BLEU)
score was employed as a metric of measurement. From our findings, our
hybrid model outperforms the baseline NMT model with a BLEU score of 1.05
on two-level translation. This indicates that the accuracy is dependent on the
level and type of hybrid used. Studies that look at in-depth pre-translation
strategies for developing translation machine model are green areas for
pidgin-English translation.
Keywords
QA Mathematics, QA75 Electronic computers. Computer science