Improving Machine Translation Quality with Automatic Named Entity Recognition

Abstract

Named entities create serious problems for state-of-the-art commercial machine translation (MT) systems and often cause translation failures beyond the local context, affecting both the overall morphosyntactic well-formedness of sentences and word sense disambiguation in the source text. We report on the results of an experiment in which MT input was processed using output from the named entity recognition module of Sheffield's GATE information extraction (IE) system. The gain in MT quality indicates that specific components of IE technology could boost the performance of current MT systems.