Publikation

System Combination Using Joint, Binarised Feature Vectors

Christian Federmann

In: Proceedings of the Second Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation (ML4HMT-12). Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation (ML4HMT-12) Mumbai India Seiten 77-84 The COLING 2012 Organizing Committee 12/2012.

Abstrakt

We describe a method for system combination based on joint, binarised feature vectors. Our method can be used to combine several black-box source systems. We first define a total order on given translation output which can be used to partition an n-best list of translations into a set of pairwise system comparisons. Using this data, we explain how an SVM-based classification model can be trained and how this classifier can be applied to combine translation output on the sentence level. We describe our experiments for the ML4HMT-12 shared task and conclude by giving a summary of our findings and by discussing future extensions and experiments using the proposed approach.

Weitere Links

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence