  In this paper, we propose a novel BTG-forest-based alignment method. Based on
a fast unsupervised initialization of parameters using variational IBM models,
we synchronously parse parallel sentences top-down and align hierarchically
under the constraint of BTG. Our two-step method can achieve the same run-time
and comparable translation performance as fast_align while it yields smaller
phrase tables. Final SMT results show that our method even outperforms in the
experiment of distantly related languages, e.g., English-Japanese.
