BI-DIRECTIONAL NEURAL MACHINE TRANSLATION FOR

Abstract

Machine translation is one of the Natural Language Processing applications, which enables the translation of text from one natural language to another. This study aimed to design and develop a bidirectional English-Sidaamu Afoo neural machine translation system, as the need system has become increasingly important due to the growing number of language users, it needs to increase its presence on the web, For effective communication and information sharing, translation of various official documents, news articles, and other written texts in both languages is necessary and last to need integrating the other high-level NLP tools, but no prior solution in this area. Recently, Neural Machine Translation has emerged as a promising approach to machine translation, delivering state-of-the-art translation quality. Unlike traditional machine translation methods, NMT uses a single neural network that can be continuously fine-tuned to improve translation performance. This study aimed to develop a bidirectional Sidaamu Afoo-English machine translation system using deep learning techniques, specifically LSTM and Transformer models. In an attempt to do this study, due to un availability of parallel data for machine translation, we opted to collect parallel data from a religious domain, specifically from Bible and Sidaamu Afoo conversation. After gathering the data, experiments were conducted using 15,000 parallel sentences from different domains. To determine the optimal model, the efficiency in terms of training time, memory usage, and BLEU score was evaluated. The results showed that the Transformer model yielded the best results, with a BLEU score of 0.413 for Sidaamu Afoo to English translation and 0.465 for English to Sidaamu Afoo translation. Future work to enhance the performance of the system could include further research and the addition of more clean data and larger corpus sizes