Automatic post-editing method using translation knowledge based on intuitive common parts continuum for statistical machine translation

Abstract

We propose a new post-editing method for statistical machine translation. The method acquires translation rules automatically as translation knowledge from a parallel corpus without depending on linguistic tools. The translation rules, which are acquired based on Intuitive Common Parts Continuum (ICPC), can deal with the correspondence of the global structure of a source sentence and that of a target sentence without requiring linguistic tools. Moreover, it generates better translation results by application of translation rules to translation results obtained through statistical machine translation. The experimentally obtained results underscore the effectiveness of applying the translation rules for statistical machine translation.