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 with particular focus on long-distance reordering for capturing the global sentence structure of a sublanguage. The proposed method learns global reordering models from a non-annotated parallel corpus and works in conjunction with conventional syntactic reordering. Experimental results on the patent abstract sublanguage show substantial gains of more than 25 points in the RIBES metric and comparable BLEU scores both for Japanese-to-English and English-to-Japanese translations.

This paper proposes evaluation methods for reading comprehension of English to Japanese translation outputs. The methods were designed not only to evaluate the performance of current systems, but to evaluate the performance of future systems had the current problems been solved. The experiments have shown that the proposed methods are capable of producing results that are statistically significant, and that improvement in certain linguistic aspects will result in significant improvement in the comprehension level.