Abstract:
Machine Translation involves multiple phases including morphological, syntax and semantic analysis of source and target languages. Despite there are numerous approaches to machine translations, handling of semantics has been an unsolved research challenge. We have been researching to exploit power of multiagent
Systems technology for machine translation by extending our rule-based machine translation system, BEES. Since there are no agent development framework specific to machine translation, our project has started by developing our own framework, MaSMT. This paper presents our research on the development of morphological analysis phase in
MaSMT. Twenty-two ordinary agents and one manager agent have been implemented to model morphological analysis of English language. In contrast, MaSMT implements 206 agents and a manager agent to handle morphological analysis in Sinhala language.
MaSMT has been developed in Java, while BEES is a Prolog implementation. Performance of morphological analysis by MaSMT and BEES has been evaluated. It
was revealed that MaSMT performs much faster than BEES for morphological analysis of English sentences with a reasonable length such as 15 words. In case of Sinhala language too, MaSMT performs better than BEES. The difference in performances of MaSMT in Sinhala and English reflects the number of morphological rules in two languages. Due to parallel execution, MaSMT shows a significant improvement in
identification of syntactic categories of words that have more than one interpretation. This feature will be reflected even better in syntactic and semantic analysis as they necessarily involve rules with multiple interpretations.
