References Extension for the Automatic Evaluation of MT by Syntactic Hybridization

Because of the variations of the languages, the coverage of the references is very important to the reference based automatic evaluation of machine translation systems. We propose a method to extend the reference set of the automatic evaluation only based on multiple manual references and their syntactic structures. In our approach, the syntactic equivalents in the reference sentences are identified and hybridized to generate new references. The new method need no external knowledge and can obtain the equivalents of long subsegments of reference sentences. The experimental results show that using the extended reference set the popular automatic evaluation metrics achieve better correlations with the human assessments.