Minimum Bayes’ risk subsequence combination for machine translation

Abstract

System combination has proved to be a successful technique in the pattern recognition field. However, several difficulties arise when combining the outputs of tasks, e.g. machine translation, that generates structured patterns. So far, machine translation system combination approaches either implement sophisticated classifiers to select one of the provided translations, or generate new sentences by combining the best subsequences of the provided translations. We present minimum Bayes' risk system combination (MBRSC), a system combination method for machine translation that gathers together the advantages of sentence-selection and subsequence-combination methods. MBRSC is able to detect and utilize the best subsequences of the provided translations to generate the optimal consensus translation with respect to a particular performance metric. Experiments show that MBRSC obtains significant improvements in translation quality, and a particularly competitive performance when applied to languages with scarce resources.