Optimal alignment for Bi-directional Statistical Machine translation

Statistical machine translation is an approach that mainly use parallel corpus for translation, in which parallel corpus alignment of the given corpus is crucial point to have better translation performance. Alignment quality is a common problem for statistical machine translation because, if sentences are miss aligned the performance of the translation processes becomes poor. This study aims to explore the effect of word level, phrase level and sentence level alignment on bi- Directional Afaan Oromo-English statistical machine translation. In order to conduct the study the corpus was collected from different sources such as criminal code, FDRE constitution, Megleta Oromia and Holly Bible. In order to make the corpus suitable for the system different preprocessing tasks applied such as true casing, sentence splitting and sentence merging has been done. A total of 6400 simple and complex sentences are used in order to train and test the system. We use 9:1 ratio for training and testing respectively. For language model we used 19300 monolingual sentence for English and 12200 for Afaan Oromo.