Multi-Engine Machine Translation Guided by Explicit Word Matching

We describe a new approach for synthetically combining the output of several different Machine Translation (MT) engines operating on the same input. The goal is to produce a synthetic combination that surpasses all of the original systems in translation quality. Our approach uses the individual MT engines as “black boxes” and does not require any explicit cooperation from the original MT systems. A decoding algorithm uses explicit word matches, in conjunction with confidence estimates for the various engines and a trigram language model in order to score and rank a collection of sentence hypotheses that are synthetic combinations of words from the various original engines. The highest scoring sentence hypothesis is selected as the final output of our system. Experiments, using several Arabicto-English systems of similar quality, show a substantial improvement in the quality of the translation output.