Bidirectional English – Afaan Oromo Machine Translation Using Hybrid Approach

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Addis Ababa University


Machine translation is one of the applications of natural language processing that studies the use of computer programs and software to translate one natural language into another in the form of text or speech. Since there is a need for translation of documents between English and Afaan Oromo languages there needs to be a mechanism to do so. Thus, this study resulted in the development of a bidirectional English-Afaan Oromo machine translation system using a hybrid approach. The research work is implemented using a hybrid of rule based and statistical approaches. Since English and Afaan Oromo have different sentence structures, we implement syntactic reordering approach which makes the structure of source sentences to be similar to the structure of target sentences. So, reordering rules are developed for simple, interrogative and complex English and Afaan Oromo sentences. In order to achieve the objective of this research work, a corpus is collected from different domain and prepared in a format suitable for use in the development process and classified as training set and test set. The reordering rules are applied on both the training and test sets in a preprocessing step. Since the system is bidirectional, two language models are developed; one for English and the other for Afaan Oromo. Translation models which assign a probability that a given source language text generates a target language text are built and a decoder which searches for the shortest path is used. Two major experiments are conducted by using two different approaches and their results are recorded. The first experiment is carried out by using a statistical approach. The result obtained from the experiment has a BLEU score of 32.39% for English to Afaan Oromo translation and 41.50% for Afaan Oromo to English translation. The second experiment is carried out by using a hybrid approach and the result obtained has a BLEU score of 37.41% for English to Afaan Oromo translation and 52.02% for Afaan Oromo to English translation. From the result, we can see that the hybrid approach is better than the statistical approach for the language pair and a better translation is acquired when Afaan Oromo is used as a source language and English is used as a target language. Key words: Machine Translation, Statistical Machine Translation, Hybrid Machine Translation, Reordering rule



Machine Translation; Statistical Machine Translation; Hybrid Machine Translation; Reordering Rule