  Compounded words are a challenge for NLP applications such as machine
translation (MT). We introduce methods to learn splitting rules from
monolingual and parallel corpora. We evaluate them against a gold standard and
measure their impact on performance of statistical MT systems. Results show
accuracy of 99.1% and performance gains for MT of 0.039 BLEU on a
German-English noun phrase translation task.
