Learning Translation Rules from Bilingual English - Filipino Corpus

Most machine translators are implemented using example based, rule based, and statistical approaches. However, each of these paradigms has its drawbacks. Example based and statistical based approaches are domain specific and requires a large database of examples to produce accurate translation results. Although rule based approach is known to produce high quality translations, a linguist is necessary in deriving the set of rules to be used. To address these problems, we present an approach that uses the rule based approach in translating from English to Filipino text. It incorporates learning of rules based on the analysis of a bilingual corpus in an attempt to eliminate the need for a linguist. The learning algorithm is based on seeded version space learning algorithm as presented by Probst (2002). Implementation of the algorithm has been modified to allow learning of non-lexically aligned languages and to adapt to the complex free word order of the Filipino language.