Improvement of word alignment in thai-english statistical machine translation by grammatical attributes identification

Abstract

This paper presents a method to handle difference of Thai and English language in an alignment process for statistical machine translation (SMT). By identification of grammar notations within both texts, the method can analyze a type of the grammatical attribute and differently handle both Thai and English words accordingly based on linguistic knowledge. This method works as a pre-process of a standard co-occurrence alignment, GIZA. An experimental result showed that this method gained 48% higher accuracy result than the widely used conventional alignment. We can conclude that a different grammatical attribute should be pre-process handled since this issue greatly affects the result of bilingual alignment and SMT.