  Speech-to-text translation (ST), which translates source language speech into
target language text, has attracted intensive attention in recent years.
Compared to the traditional pipeline system, the end-to-end ST model has
potential benefits of lower latency, smaller model size, and less error
propagation. However, it is notoriously difficult to implement such a model
without transcriptions as intermediate. Existing works generally apply
multi-task learning to improve translation quality by jointly training
end-to-end ST along with automatic speech recognition (ASR). However, different
tasks in this method cannot utilize information from each other, which limits
the improvement. Other works propose a two-stage model where the second model
can use the hidden state from the first one, but its cascade manner greatly
affects the efficiency of training and inference process. In this paper, we
propose a novel interactive attention mechanism which enables ASR and ST to
perform synchronously and interactively in a single model. Specifically, the
generation of transcriptions and translations not only relies on its previous
outputs but also the outputs predicted in the other task. Experiments on TED
speech translation corpora have shown that our proposed model can outperform
strong baselines on the quality of speech translation and achieve better speech
recognition performances as well.
