Text prediction with fuzzy alignments

Abstract

Text prediction is a form of interactive machine translation that is well suited to skilled translators. In recent work it has been shown that simple statistical translation models can be applied within a usermodeling framework to improve translator productivity by over 10% in simulated results. For the sake of efficiency in making real-time predictions, these models ignore the alignment relation between source and target texts. In this paper we introduce a new model that captures fuzzy alignments in a very simple way, and show that it gives modest improvements in predictive performance without significantly increasing the time required to generate predictions.