Improving Chinese-English neural machine translation with detected usages of function words

Abstract

One of difficulties in Chinese-English machine translation is that the grammatical meaning expressed by morphology or syntax in target translations is usually determined by Chinese function words or word order. In order to address this issue, we develop classifiers to automatically detect usages of common Chinese function words based on Chinese Function usage Knowledge Base (CFKB) and initially propose a function word usage embedding model to incorporate detection results into neural machine translation (NMT). Experiments on the NIST Chinese-English translation task demonstrate that the proposed method can obtain significant improvements on the quality of both translation and word alignment over the NMT baseline.