A Neural Machine Translation Approach for Translating Different Languages in English

Abstract

Background and Objective: Neural Machine Translation (NMT) is capable of predicting the likely consequences of a given string of words. Twenty-seven languages were used in the study and by using these 27 language datasets, the model translation capacity has been evaluated. Some sentences of every language compared with the output of Google Translate. Materials and Methods: A recurrent neural network named long short-term memory has been used. Here, the ADAM optimization algorithm and Soft Max Activation function have been used. The BLEU (Bilingual Evaluation Understudy) is used for evaluating translation quality. Results: The research has shown that Western languages have given better BLEU (Bilingual Evaluation Understudy) scores than Asian languages. Especially the Latin script languages have given better translation quality than other script languages. Among 27 languages, the research has worked with four languages whose translations even do not add the Google translator. The translation quality is good according to the BLEU score matrix algorithm. The established neural machine translation system has given a good translation in these aspects. Conclusion: So, in this research, 27 languages are translated into English by the Neural Machine Translation model. The research can contribute to the field of machine translation.