  Simultaneous machine translation (SiMT) outputs translation while receiving
the streaming source inputs, and hence needs a policy to determine where to
start translating. The alignment between target and source words often implies
the most informative source word for each target word, and hence provides the
unified control over translation quality and latency, but unfortunately the
existing SiMT methods do not explicitly model the alignment to perform the
control. In this paper, we propose Gaussian Multi-head Attention (GMA) to
develop a new SiMT policy by modeling alignment and translation in a unified
manner. For SiMT policy, GMA models the aligned source position of each target
word, and accordingly waits until its aligned position to start translating. To
integrate the learning of alignment into the translation model, a Gaussian
distribution centered on predicted aligned position is introduced as an
alignment-related prior, which cooperates with translation-related soft
attention to determine the final attention. Experiments on En-Vi and De-En
tasks show that our method outperforms strong baselines on the trade-off
between translation and latency.
