In this paper, we address the word alignment problem for statistical machine translation. We aim at creating a sym- metric word alignment allowing for reli- able one-to-many and many-to-one word relationships. We perform the iterative alignment training in the source-to-target and the target-to-source direction with the well-known IBM and HMM alignment models. Using these models, we robustly estimatethelocalcostsofaligningasource word and a target word in each sentence pair. Then, we use efficient graph algo- rithms to determine the symmetric align- ment with minimal total costs (i.e. max- imal alignment probability). We evalu- ate the automatic alignments created in this way on the German–English Verb- mobil task and the French–English Cana- dian Hansards task. We show statistically s...

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