| Abstract: | Machine translation (MT) is one of the applications of natural language processing which 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swiftly superseded SMT as the predominant method of MT, developed with the development of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. 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 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 it 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
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 80/20 ratio. These data were preprocessed in the suitable format
in 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 has been proposed
in the Sequence-to-Sequence concept. In order to evaluate the efficiency of the proposed system,
BLUE score metrics is used, and for testing the efficiency of attention mechanism, we have
developed 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. |
