YEMSA TO AMHARIC MACHINE TRANSLATION USINGDEEP LEARNING TECHNIQUES

Abstract

In today's globalized world, the barriers of distance and language have been greatly diminished, transforming our world into a closely interconnected global community. As a consequence, human languages have taken on an international character, enabling effective communication across borders. Traditionally, human translation is costly and inconvenient; several kinds of research are currently conducted to resolve this problem with machine translation techniques. |So, it is automatic, which means it translates one language to another using a computer software system. In this study, Yemsa to Amharic machine translation and vice versa are used by deep learning techniques. Due to increased speaker numbers, to address the issue of endangered Yemsa language and enhance the language's content on the World Wide Web. A numberof indigenous knowledge medicines called Samo Heta and other traditional and religious names are found in the Yemsa language. We utilized the current STOA method of deep learning. The work was executed using a seq-to-seq encoder-decoder architecture. The proposed study was conducted experiments on LSTM, Bi-LSTM, LSTM with attention, GRU and transformer models. We collected a dataset of about 6,000 parallel sentences with 11690 and 12491 vocabularies. In order to translate textinto sentence sequence, we applied the preprocessing technique and used Morfessor tools. The proposed studies utilize the 80/20 splitting technique for dividing the dataset into training and testing sets. The next step is training and testing models on a corresponding with training and testing dataset. The experiment was conducted on LSTM, Bi-LSTM, LSTM+ attention, GRU and Transformer models. Among those models, the transformer model outperforms other models by 99.4% accuracy, 0.0113 loss. And BLEU scores of 9.7 and 9.8 from Yemsa to Amharic and Amharic to Yemsa respectively. The primary limitation of the investigation is the insufficient availability of a substantial dataset to conduct comprehensive experimentation. As a result, there is a necessity to generate parallel corpora in order to conduct comparable research. Finally, the findings of the study show that utilizing deep learning techniques, particularly the transformer model, can significantly improve Yemsa to Amharic machine translation accuracy and BLEU scores.