This paper presents a maximum entropy word alignment algorithm for Arabic- English based on supervised training data. We demonstrate that it is feasible to cre- ate training material for problems in ma- chine translation and that a mixture of su- pervised and unsupervised methods yields superior performance. The probabilistic model used in the alignment directly mod- els the link decisions. Significant improve- ment over traditional word alignment tech- niques is shown as well as improvement on several machine translation tests. Perfor- mance of the algorithm is contrasted with human annotation performance.

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