We describe a MT system for English to Hindi in which we use a pre-processing module to transform English data into Hindi-Order to get better phrase alignments. SMT depends heavily on parallel training data available for a language pair. We also show use of machine translation (MT) in improving speech recognition output in the scenario of a computer assisted translation (CAT) system. This thesis addresses problems of data-sparsity and translation quality of a SMT system for English-Hindi and Indo-Aryan language pairs. Statistical machine translation (SMT) depends heavily on parallel training data available for a language pair. 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.

Pidgin translator