Impact of Statistical Language Model on Example Based Machine Translation System between Kazakh and Turkish Languages

Abstract

In this paper a hybrid example based machine translation system between Kazakh and Turkish languages is presented. The system mainly based on example based machine translation method which is supported by a statistical language model for the target language. Translation templates are learned at morphological level from a bilingual parallel corpus of Turkish and Kazakh languages. Translations can be performed at both directions using these learned translation templates. Our main aim with this hybrid example based machine translation system is to obtain more accurate translation results by pre-gained knowledge from target language resource. One of the reasons that we propose this hybrid approach is that monolingual language resources are more widely available than bilingual language resources.