Design and implementation of Attention-based Neural Machine Translation from English- Wolaytta

Design and implementation of Attention-based Neural Machine Translation from English- Wolaytta

ABSTRACT

Machine translation (MT) is one of the applications of natural language processing that involves using computers to translate from one source language to another target language. For many years, Statistical Machine Translation (SMT) dominated the field of machine translation technology. Long sentences are broken up into small pieces in classical statistical machine translation, which results in poor levels of accuracy. Neural Machine Translation (NMT) is a new paradigm that swiftly superseded SMT as the predominant method of MT, developed with the development of deep learning. The NMT approach differs from SMT systems as all parts of the neural translation model are trained jointly (end-to-end) to maximize the translation performance. In an encoder-decoder design, the entire source sequence's input is condensed into a single context vector, that is then sent to the decoder to create the output sequence. The major drawback of the encoder-decoder model is that it can only work on short sequences. It is difficult for the encoder model to memorize long sequences and convert them into a fixed-length vector. One realistic solution to this problem is the attention mechanism. The attention mechanism predicts the next word by concentrating on a few relevant parts of the sequence rather than looking on the entire sequence. Hence, the objective of this research work is to develop a neural machine translation system for English-Wolaytta using an attention mechanism. The English-Wolaytta machine translation system has been trained on parallel corpus covering the religious, and frequently used sentences or phrases which can be used in day-to-day communication. A total of 27351 parallel English-Wolaytta sentences were prepared and the system is trained and tested using an 80/20 ratio. These data were preprocessed in a suitable format in a way to be used in neural machine translation. For building the proposed English-Wolaytta NMT model, an LSTM encoder and decoder architecture with an attention mechanism have been proposed in the Sequence-to-Sequence concept. In order to evaluate the efficiency of the proposed system, BLUE score metrics are used, and for testing the efficiency of the attention mechanism, we have developed a non-attention model and compared it with the attention mechanism. Hence, we have proved that the attention mechanism has a better translation and has achieved a BLEU score of 5.16 and 88.65 accuracy.

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