Rule Extraction Applied in Language Translation

Machine translation (MT) has been used to address inherent problems from human translators. However, the quality of machine translations are usually unacceptable. Researches have focused on improving quality by incorporating machine learning for translation. An example of which is TWiRL which translates English to Filipino sentences. However, TWiRL’s approach presented a strict requirement of a grammar that limits the system’s flexibility and feasibility. This architecture will not be applicable for reverse Filipino to English translation. To address the problems of TWiRL, REAL Translation provides an alternative approach in translation by removing the grammar requirement. Instead, POS taggers and Semantic Analyzers takes its place to deduce rules from bilingual corpus.