Translating compounds is an important problem in machine translation. Since many
compounds have not been observed during training, they pose a challenge for
translation systems. Previous decompounding methods have often been restricted to a
small set of languages as they cannot deal with more complex compound forming
processes. We present a novel and unsupervised method to learn the compound parts
and morphological operations needed to split compounds into their compound parts.
The method uses a bilingual corpus to learn the morphological operations required
to split a compound into its parts. Furthermore, monolingual corpora are used to
learn and filter the set of compound part candidates. We evaluate our method within
a machine translation task and show significant improvements for various languages
to show the versatility of the approach.