Oluwatomiyin, Saviour and Misra, Sanjay and Wejin, John and Agrawal, Akshat and Jonathan, Oluranti (2022) A Hybrid Translation Model for Pidgin English to English Language Translation. In: Data, Engineering and Applications, 12 October 2022, Online.
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Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | Patricia Nwokealisi |
| Date Deposited: | 19 Jul 2024 14:46 |
| Last Modified: | 19 Jul 2024 14:46 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/18266 |
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