  Simultaneous machine translation models start generating a target sequence
before they have encoded or read the source sequence. Recent approaches for
this task either apply a fixed policy on a state-of-the art Transformer model,
or a learnable monotonic attention on a weaker recurrent neural network-based
structure. In this paper, we propose a new attention mechanism, Monotonic
Multihead Attention (MMA), which extends the monotonic attention mechanism to
multihead attention. We also introduce two novel and interpretable approaches
for latency control that are specifically designed for multiple attentions
heads. We apply MMA to the simultaneous machine translation task and
demonstrate better latency-quality tradeoffs compared to MILk, the previous
state-of-the-art approach. We also analyze how the latency controls affect the
attention span and we motivate the introduction of our model by analyzing the
effect of the number of decoder layers and heads on quality and latency.
