From the JW300 public dataset, we used 22,047 sentence pairs for training our model,1000 for tuning, and 2520 for testing.
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. To proffer a solution, researchers in machine translation from Pidgin English to the English language have leveraged only unsupervised and supervised Neural Machine Translation-based models. 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. Despite the diversity, one common point of unification, especially among the West African communities is the spoken pidgin-English language. The African continent is made up of people with rich diverse cultures and spoken languages.
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