Paper: Symmetric Word Alignments For Statistical Machine Translation

ACL ID C04-1032
Title Symmetric Word Alignments For Statistical Machine Translation
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2004

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...