Abstract
We present a Machine-Learning-based framework for hybrid Machine Translation. Our approach combines translation output from several black-box source systems. We define an extensible, total order on translation output and use this to decompose the n-best translations into pairwise system comparisons. Using joint, binarised feature vectors we train an SVM-based classifier and show how its classification output can be used to generate hybrid translations on the sentence level. Evaluations using automated metrics shows promising results. An interesting finding in our experiments is the fact that our approach allows to leverage good translations from otherwise bad systems as the combination decision is taken on the sentence instead of the corpus level. We conclude by summarising our findings and by giving an outlook to future work, e.g., on probabilistic classification or the integration of manual judgements.
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Federmann, C. (2012). A Machine-Learning Framework for Hybrid Machine Translation. In: Glimm, B., Krüger, A. (eds) KI 2012: Advances in Artificial Intelligence. KI 2012. Lecture Notes in Computer Science(), vol 7526. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33347-7_4
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DOI: https://doi.org/10.1007/978-3-642-33347-7_4
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