Dependency-Based Decipherment for Resource-Limited Machine Translation.

We introduce dependency relations into deciphering foreign languages and show that dependency relations help improve the state-ofthe-art deciphering accuracy by over 500%. We learn a translation lexicon from large amounts of genuinely non parallel data with decipherment to improve a phrase-based machine translation system trained with limited parallel data. In experiments, we observe BLEU gains of 1.2 to 1.8 across three different test sets.