Beyond Parallel Data: Joint Word Alignment and Decipherment Improves Machine Translation

Inspired by previous work, where decipher-ment is used to improve machine translation, we propose a new idea to combine word align-ment and decipherment into a single learning process. We use EM to estimate the model pa-rameters, not only to maximize the probabil-ity of parallel corpus, but also the monolingual corpus. We apply our approach to improve Malagasy-English machine translation, where only a small amount of parallel data is avail-able. In our experiments, we observe gains of 0.9 to 2.1 Bleu over a strong baseline.