A SomAgent statistical machine translation

Abstract

The paper describes the process by which the word alignment task performed within SOMAgent works in collaboration with the statistical machine translation system in order to learn a phrase translation table. We studied improvements in the quality of translation using syntax augmented machine translation. We also experimented with different degrees of linguistic analysis from the lexical level to a syntactic or semantic level, in order to generate a more precise alignment. We developed a contextual environment using the Self-Organizing Map, which can model a semantic agent (SOMAgent) that learns the correct meaning of a word used in context in order to deal with specific phenomena such as ambiguity, and to generate more precise alignments that can improve the first choice of the statistical machine translation system giving linguistic knowledge.