  The black-box nature of end-to-end speech translation (E2E ST) systems makes
it difficult to understand how source language inputs are being mapped to the
target language. To solve this problem, we would like to simultaneously
generate automatic speech recognition (ASR) and ST predictions such that each
source language word is explicitly mapped to a target language word. A major
challenge arises from the fact that translation is a non-monotonic sequence
transduction task due to word ordering differences between languages -- this
clashes with the monotonic nature of ASR. Therefore, we propose to generate ST
tokens out-of-order while remembering how to re-order them later. We achieve
this by predicting a sequence of tuples consisting of a source word, the
corresponding target words, and post-editing operations dictating the correct
insertion points for the target word. We examine two variants of such operation
sequences which enable generation of monotonic transcriptions and non-monotonic
translations from the same speech input simultaneously. We apply our approach
to offline and real-time streaming models, demonstrating that we can provide
explainable translations without sacrificing quality or latency. In fact, the
delayed re-ordering ability of our approach improves performance during
streaming. As an added benefit, our method performs ASR and ST simultaneously,
making it faster than using two separate systems to perform these tasks.
