Frequency-driven late fusion-based word decomposition approach on the phrase-based statistical machine translation systems

Tatlıcıoğlu, Mehmet
Machine translation is the process of translating texts from a natural language to another by computers based on linguistic motivations, statistical approaches, or the combination of them. In this study, the frequency-driven late fusion-based word decomposition approach is introduced to improve the translation quality of the phrase-based statistical machine translation system from Turkish to English. This late fusion-based approach is compared with the standalone statistical and rule-based word decomposition approaches when the corpus size changes. This study differs from others by introducing the novel frequency-driven late fusion-based word decomposition method to boost the BLEU score. While the benchmark study in the literature reports a 25.22 BLEU score, the proposed late fusion-based system boosts the accuracy up to a 26.22 BLEU score. This novel approach fuses both of the rule-based and stochastic word decomposition methods. Because of the agglutinative nature of Turkish language, the results can be extended to the other agglutinative languages as well.


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Citation Formats
M. Tatlıcıoğlu, “Frequency-driven late fusion-based word decomposition approach on the phrase-based statistical machine translation systems,” M.S. - Master of Science, Middle East Technical University, 2013.