Models and Inference for Prefix-Constrained Machine Translation

摘要

We apply phrase-based and neural models to a core task in interactive machine translation: suggesting how to complete a partial translation. For the phrase-based sys-tem, we demonstrate improvements in suggestion quality using novel objective functions, learning techniques, and inference algorithms tailored to this task. Our contributions include new tunable metrics, an improved beam search strategy, an n -best extraction method that increases suggestion diversity, and a tuning procedure for a hierarchical joint model of alignment and translation. The combination of these techniques improves next-word suggestion accuracy dramatically from 28.5% to 41.2% in a large-scale English-German experiment. Our recurrent neural translation system increases accuracy yet further to 53.0%, but inference is two orders of magnitude slower. Manual error analysis shows the strengths and weaknesses of both approaches.