  Growing needs in localising audiovisual content in multiple languages through
subtitles call for the development of automatic solutions for human subtitling.
Neural Machine Translation (NMT) can contribute to the automatisation of
subtitling, facilitating the work of human subtitlers and reducing turn-around
times and related costs. NMT requires high-quality, large, task-specific
training data. The existing subtitling corpora, however, are missing both
alignments to the source language audio and important information about
subtitle breaks. This poses a significant limitation for developing efficient
automatic approaches for subtitling, since the length and form of a subtitle
directly depends on the duration of the utterance. In this work, we present
MuST-Cinema, a multilingual speech translation corpus built from TED subtitles.
The corpus is comprised of (audio, transcription, translation) triplets.
Subtitle breaks are preserved by inserting special symbols. We show that the
corpus can be used to build models that efficiently segment sentences into
subtitles and propose a method for annotating existing subtitling corpora with
subtitle breaks, conforming to the constraint of length.
