Optimal Alignment For Bi-Directional Afaan Oromo-English Statistical Machine Translation
Volume 3 - Issue 7, July 2019 Edition
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Author(s)
Yitayew Solomon, Million Meshesha, Wendewesen Endale
Keywords
Afaan Oromo; Statistical Machine Translation; Word Level Alignment; Phrase Level Alignment; Sentence Level Alignment
Abstract
Statistical machine translation is an approach that mainly use parallel corpus for translation, in which 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. Experimental results show that better performance of 47% and 27% BLEU score was registered using phrase level alignment with max phrase length 16 from Afaan Oromo-English machine translation and vice versa, respectively. Grammar structure and variation in concept definition and correspondence are the major challenge during machine translation (MT) which need further research.
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