Statistical Machine Translation Support Improves Human Adjective Translation

In this paper we present a study in computer-assisted translation, investigating whether nonprofessional translators can profit directly from automatically constructed bilingual phrase pairs. Our support is based on state-of-the-art statistical machine translation (smt), consisting of a phrase table that is generated from large parallel corpora, and a large monolingual language model. In our experiment, human translators were asked to translate adjective–noun pairs in context in the presence of suggestions created by the smt model. Our results show that smt support results in an acceptable slowdown in translation time while significantly improving translation quality.