Factored Statistical Machine Translation for German-English

Darryl Yunus Sulistyan

doi:10.33555/ejaict.v5i1.47

Darryl Yunus Sulistyan Swiss German University

DOI: https://doi.org/10.33555/ejaict.v5i1.47

Keywords: Factored Machine Translation, MOSES, Part-of-Speech, Lemma, Language Model

Abstract

Machine Translation is a machine that is going to automatically translate given sentences in a language to other particular language. This paper aims to test the effectiveness of a new model of machine translation which is factored machine translation. We compare the performance of the unfactored system as our baseline compared to the factored model in terms of BLEU score. We test the model in German-English language pair using Europarl corpus. The tools we are using is called MOSES. It is freely downloadable and use. We found, however, that the unfactored model scored over 24 in BLEU and outperforms the factored model which scored below 24 in BLEU for all cases. In terms of words being translated, however, all of factored models outperforms the unfactored model.