To improve word alignment and MT in a lowresource setting, we design a multitask learning approach that utilizes parallel data of a third language, called the pivot language ( §3).
we derive an efficient and easy-to-implement MAP-EM-like algorithm that jointly trains sourcetarget, source-pivot and pivot-target alignment models, each on its own bitext, such that each model benefits from observations made by the other two.