Abstract
Large amounts of good quality parallel texts (translation memory) are required for statistical machine translation methods. While collecting large amounts of parallel texts has been studied so far, a methodology of managing qualityby revising sentences for parallel text improvement (clean-up)has not been well-known. Here parallel text ranking based on quantification about word alignments is described. We show ranking experimental results for the clean-up using about 400 thousand parallel Japanese-English sentences.
