  We investigate the problem of simultaneous machine translation of long-form
speech content. We target a continuous speech-to-text scenario, generating
translated captions for a live audio feed, such as a lecture or play-by-play
commentary. As this scenario allows for revisions to our incremental
translations, we adopt a re-translation approach to simultaneous translation,
where the source is repeatedly translated from scratch as it grows. This
approach naturally exhibits very low latency and high final quality, but at the
cost of incremental instability as the output is continuously refined. We
experiment with a pipeline of industry-grade speech recognition and translation
tools, augmented with simple inference heuristics to improve stability. We use
TED Talks as a source of multilingual test data, developing our techniques on
English-to-German spoken language translation. Our minimalist approach to
simultaneous translation allows us to easily scale our final evaluation to six
more target languages, dramatically improving incremental stability for all of
them.
