A Hybrid Translation Model for Pidgin English to English Language Translation

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dc.creatorOluwatomiyin, Saviour, Misra, Sanjay, Wejin, John, Agrawal, Akshat, Jonathan, Oluranti
dc.date2022
dc.date.accessioned2025-04-15T11:55:13Z
dc.descriptionThe 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.
dc.formatapplication/pdf
dc.identifierhttp://eprints.covenantuniversity.edu.ng/18266/
dc.identifier.urihttps://repository.covenantuniversity.edu.ng/handle/123456789/48761
dc.languageen
dc.subjectQA Mathematics, QA75 Electronic computers. Computer science
dc.titleA Hybrid Translation Model for Pidgin English to English Language Translation
dc.typeConference or Workshop Item

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