Improving automatic Chinese–Japanese patent translation using bilingual term extraction

The identification of terms in scientific and patent documents is a crucial issue for applications like information retrieval, text categorization, and also for machine translation. This paper describes a method to improve Chinese–Japanese statistical machine translation of patents by re-tokenizing the training corpus with aligned bilingual multi-word terms. We automatically extract multi-word terms from monolingual corpora by combining statistical and linguistic filtering methods. An automatic alignment method is used to identify corresponding terms. The most promising bilingual multi-word terms are extracted by setting some threshold on translation probabilities and further filtering by considering the components of the bilingual multi-word terms in characters as well as the ratio of their lengths in words. We also use kanji (Japanese)–hanzi (Chinese) character conversion to confirm and extract more promising bilingual multi-word terms. We obtain a high quality of correspondence with 93% in bilingual term extraction and a significant improvement of 1.5 BLEU score in a translation experiment.