DEVELOP A Bi-DIRECTIONAL ENGLISH - NUER MACHINE TRANSLATION USING DEEP LEARNING APPROACH

Abstract

The advancement of deep learning has revolutionized natural language processing, with machine translation playing a pivotal role in bridging linguistic barriers. This research focuses on developing a bi-directional English-Nuer machine translation system using deep learning techniques. The primary challenge is the lack of linguistic resources for the Nuer language, hindering its technological representation and global accessibility. To address this, the study constructed a parallel corpus of 46,134 English-Nuer sentence pairs and employed models such as GRU, Bi-GRU, LSTM, LSTM with attention and transformer mechanisms. The findings revealed that the Transformer model achieved superior BLEU scores compared to the other architectures, scoring 0.2567 for Nuer-to-English and 0.2431 for English-to-Nuer translations. The results highlight the potential of the proposed deep learning-based machine translation for low-resource languages. As future work, the researcher highlights to explore integrating speech-to-text and textto-speech capabilities to enhance usability.