Abstract:
Humans use a natural language (NL) in order to convey meanings from one entity or group to
another. This NL should be mutually understandable by both communicating entities, which is
not imaginary living in a world having a population of more than 7 billion. So, there should be a
translator between the two, of which most of the time is a human. But, human translator is
expensive and inconvenient. The emergence of Natural Language Processing (NLP) and
Machine Translation (MT) has solved this issue.MT is an automatic translation of a source
language to a target language. Allowing the use of neural network models to learn a statistical
model for MT, Neural Machine Translation (NMT), aims at building a single neural network that
can be jointly tuned to maximize translation performance. In this work, an attention based
Amharic to Afaan Oromo NMT system has been developed. The system is developed based on
Encoder-Decoder model by using a Bi-directional Gated Recurrent Unit (BGRU). In order to
compare the performance of the system, we have also implemented our system before applying
attention mechanism. The non-attention based system with basic encoder-decoder architecture,
have some limitations. As the length of the sentence increase the interdependency of words will
loosely increase. This shows that the non-attention based architecture works well with shorter
sentences but highly suffer to translate longer sentences. Moreover, as each word in the sentence
is visited, it must be assigned a new identity number in order to identify a word by a unique index
at the time it encountered it in the data. But when the length of the dictionary increases, the
dimension of word vector needed becomes higher. We have observed that these problems have
been solved by applying an attention mechanism to the system. Prior to this work there was no
other NMT system translating Amharic to Afaan Oromo. So, in this work attention based
Amharic to Afaan Oromo neural machine translation has been developed and its performance
was compared to that of non-attention based Amharic to Afaan Oromo neural machine
translation system.
For the evaluation purpose, BLEU score evaluation was used. We have recorded a BLEU score
of 61.49 for the non-attention based system and 67.82 for the attention based Amharic to Afaan
Oromo Neural Machine Translation
