Bi-Directional English-Afan Oromo Machine Translation Using Convolutional Neural Network
| dc.contributor.advisor | Menore, Tekeba (Mr.) | |
| dc.contributor.author | Arfaso, Birhanu | |
| dc.date.accessioned | 2020-03-06T07:00:14Z | |
| dc.date.accessioned | 2023-11-04T15:14:39Z | |
| dc.date.available | 2020-03-06T07:00:14Z | |
| dc.date.available | 2023-11-04T15:14:39Z | |
| dc.date.issued | 2019-10-14 | |
| dc.description.abstract | Many languages are spoken across the world which can bring communication gaps where two people that speak different languages cannot communicate. Usually, this communication gap is solved by using a human interpreter. However, the use of human interpreters is expensive and inconvenient. Many researches are being done to resolve this problem using machine translation techniques. Machine translation is an automatic translation of a source language to a target language. This can be speech to speech or text to text translation. In this work, a bi-directional text based machine translation for English and Afan Oromo languages pair using convolutional neural networks is proposed. We started our study with objective of improving the previous work on English to Afan Oromo machine translation by making the translation bi-directional by applying convolutional neural network on translations between these language pair. In order to achieve our objective, we collected parallel corpus data from different sources and divided into training and testing sets. We have used 80% of total dataset for training and 20% of total dataset for testing. Three systems were implemented where the first system uses a word based statistical approach that used as a baseline, while the second system with recurrent neural network approach is used as a competitive model and lastly, the third system with convolutional neural networks for the bi-directional translation between Afan Oromo and English languages. After training and testing these systems on corresponding training and testing datasets, the convolutional neural network achieved 3.86 BLEU score improvement on translation from English to Afan Oromo and 3.32 BLEU score on translation from Afan Oromo to English translation than baseline system. Also convolutional neural network approach has shown an improvement of 1.58 BLEU score on translation from English to Afan Oromo and 1.51 BLEU score on translation from Afan Oromo to English translation than recurrent neural network approach. The convolutional neural network approach is faster on training than recurrent neural network approach. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/20924 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Machine translation | en_US |
| dc.subject | English-Afan Oromo machine translation | en_US |
| dc.subject | recurrent neural network | en_US |
| dc.subject | convolutional neural network bi-directional machine translation | en_US |
| dc.title | Bi-Directional English-Afan Oromo Machine Translation Using Convolutional Neural Network | en_US |
| dc.type | Thesis | en_US |