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.

本文提出了一种名为 Monotonic Multihead Attention（MMA）的新型注意力机制，可应用于同时翻译的机器翻译任务，并介绍了两种特定于多头注意力的新颖且可解释的延迟控制方法，与最先进的 Milk 方法相比，MMA 具有更好的延迟 - 质量平衡，同时分析了延迟控制对关注范围的影响，通过分析解码器层数和头数对质量和延迟的影响来证明模型的引入。