How to Produce Unseen Teddy Bears: Improved Morphological Processing of Compounds in SMT

Compounding in morphologically rich languages is a highly productive process which often causes SMT approaches to fail because of unseen words. We present an approach for translation into a compounding language that splits compounds into simple words for training and, due to an underspecified representation, allows for free merging of simple words into compounds after translation. In contrast to previous approaches, we use features projected from the source language to predict compound mergings. We integrate our approach into end-to-end SMT and show that many compounds matching the reference translation are produced which did not appear in the training data. Additional manual evaluations support the usefulness of generalizing compound formation in SMT.