Improving Sublanguage Translation via Global Pre-ordering

Abstract

When translating formal documents, capturing the sentence structure specific to the sublanguage is extremely necessary to obtain high-quality translations. This paper proposes a novel global reordering method that focuses on long-distance reordering to capture the global sentence structure of a sublanguage. The proposed method learns global reordering models without syntactic parsing from a non-annotated parallel corpus and works in conjunction with conventional syntactic reordering. The experimental results regarding patent abstract sublanguage show concrete improvements in translation quality, both for Japanese-to-English and English-to-Japanese translations.