  The study of the attention mechanism has sparked interest in many fields,
such as language modeling and machine translation. Although its patterns have
been exploited to perform different tasks, from neural network understanding to
textual alignment, no previous work has analysed the encoder-decoder attention
behavior in speech translation (ST) nor used it to improve ST on a specific
task. In this paper, we fill this gap by proposing an attention-based policy
(EDAtt) for simultaneous ST (SimulST) that is motivated by an analysis of the
existing attention relations between audio input and textual output. Its goal
is to leverage the encoder-decoder attention scores to guide inference in real
time. Results on en->{de, es} show that the EDAtt policy achieves overall
better results compared to the SimulST state of the art, especially in terms of
computational-aware latency.
