In this article, compound processing for translation into German in a factored statistical MT system is investigated. Compounds are handled by splitting them prior to training, and merging the parts after translation. I have explored eight merging strategies using different combinations of external knowledge sources, such as word lists, and internal sources that are carried through the translation process, such as symbols or parts-of-speech. I show that for merging to be successful, some internal knowledge source is needed. I also show that an extra sequence model for part-ofspeech is useful in order to improve the order of compound parts.
