GE’EZ-AMHARIC MACHINE TRANSLATION USING DEEP LEARNING

GENET, WORKU

dc.contributor.author	GENET, WORKU
dc.date.accessioned	2021-08-13T06:40:50Z
dc.date.available	2021-08-13T06:40:50Z
dc.date.issued	2021-05-11
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/12389
dc.description.abstract	Neural machine translation (NMT) which has come to be the breakthrough in the field of machine translation is now greatly being used by many translation services such as google translate. This generic deep learning approach of machine translation (MT) with the help of attention mechanism is used as the core method of our Ge’ez-Amharic translation. Despite their low accuracy, low speed of translation, and involvement of linguistic professionals, other methods of Ge’ez- Amharic translation such as using Statistical Machine Translation (SMT), morpheme-based have already been researched. The complex nature of design the model for these approaches is a limitation. We used a unidirectional model which only translates from Ge’ez to Amharic. We designed an NMT encoder-decoder translation model based on attention mechanism that contains two Long Short- Term Memory (LSTM) layers with 500 hidden units both in the encoder and decoder parts. The model takes source sentence as input in the encoder side and generates a target sentence as output in the decoder side, generating a single word at a time. We used attention mechanism to handle long term dependencies in long sentences by paying attention to the parts of the input sentence which contain relevant information in generating a single word in the target sentence. We have collected 50k Ge’ez-Amharic parallel sentences and used different portions of this data for the different experiments. We used an OpenNMT for developing our model and Bilingual Evaluation Under Study (BLEU) is used in evaluating the translation quality of our model. We trained our model for the different experiments on Colab and we found the best performing translation with BLEU score of 15.4%. Despite the hungry nature of NMT models for data and the costly available data for the corpus, our model has performed well with the amount of corpus collected.	en_US
dc.language.iso	en_US	en_US
dc.subject	computer science	en_US
dc.title	GE’EZ-AMHARIC MACHINE TRANSLATION USING DEEP LEARNING	en_US
dc.type	Thesis	en_US