In this dissertation, we examine applications of neural machine translation to computer aided translation, building tools for human translators. We present a neural approach to interactive translation prediction (a form of “auto-complete” for human translators) and demonstrate its effectiveness through both simulation studies, where it outperforms a phrase-based statistical machine translation approach, and a user study. We find that about half of the translators in the study are faster using neural interactive translation prediction than they are when post-editing output of the same underlying machine translation system, and most translators express positive reactions to the tool. We perform an analysis of some challenges that neural machine translation systems face, particularly with respect to novel words and consistency. We experiment with methods of improving translation quality at a fine-grained level to address those challenges. Finally, we bring these two areas - interactive and adaptive neural machine translation - together in a simulation that shows that their combination has a positive impact on novel word translation and other metrics.
Speaker Biography
Rebecca Knowles is a PhD candidate in the Center for Language and Speech Processing and Computer Science Department at Johns Hopkins University, where she is advised by Philipp Koehn. She received an NSF Graduate Research Fellowship in 2013. Her research focuses on machine translation and computer aided translation (building tools for human translators). She received her B.S. in mathematics and linguistics from Haverford College.