A Maximum Entropy Word Aligner for Arabic-English Machine Translation 
 This paper presents a maximum entropy word alignment algorithm for Arabic-English based on supervised training data . We demonstrate that it is feasible to create training material for problems in machine translation and that a mixture of supervised and unsupervised methods yields superior performance . The probabilistic model used in the alignment directly models the link decisions . Significant improvement over traditional word alignment techniques is shown as well as improvement on several machine translation tests . Performance of the algorithm is contrasted with human annotation performance . 