![]() ![]() Sánchez-Martínez F, Forcada ML (2009) Inferring shallow-transfer machine translation rules from small parallel corpora. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA, pp 12–21. Ravi S, Knight K (2011) Deciphering Foreign Language. ![]() M.S Thesis, Comp Sci, IIIT, Hyderabad, India. Karan S (2015) Methods for leveraging lexical information in SMT. Accessed Īrtetxe M, Labaka G, Agirre E (2020) Unsupervised statistical machine translation. In: Proceedings of the 2003 human language technology conference of the north american chapter of the association for computational linguistics. Koehn P, Och FJ, Marcu D (2003) Statistical phrase-based translation. Marcu D, Wong W (2002) A phrase-based, joint probability model for statistical machine Translation Daniel Marcu. īrown PF et al (1990) A statistical approach to machine translation. Lopez A (2007) A survey of statistical machine translation. Available: Nigeria mobile internet user penetration 2025 | Statista. Mobile internet user penetration in Nigeria from 2015 to 2025”. Keywordsībc starts pidgin digital service for west Africa audiences (2017). Studies that look at in-depth pre-translation strategies for developing translation machine model are green areas for pidgin-English translation. This indicates that the accuracy is dependent on the level and type of hybrid used. From our findings, our hybrid model outperforms the baseline NMT model with a BLEU score of 1.05 on two-level translation. The Bi-Lingual Evaluation Understudy (BLEU) score was employed as a metric of measurement. 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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