  Recently, sign language researchers have turned to sign language interpreted
TV broadcasts, comprising (i) a video of continuous signing and (ii) subtitles
corresponding to the audio content, as a readily available and large-scale
source of training data. One key challenge in the usability of such data is the
lack of sign annotations. Previous work exploiting such weakly-aligned data
only found sparse correspondences between keywords in the subtitle and
individual signs. In this work, we propose a simple, scalable framework to
vastly increase the density of automatic annotations. Our contributions are the
following: (1) we significantly improve previous annotation methods by making
use of synonyms and subtitle-signing alignment; (2) we show the value of
pseudo-labelling from a sign recognition model as a way of sign spotting; (3)
we propose a novel approach for increasing our annotations of known and unknown
classes based on in-domain exemplars; (4) on the BOBSL BSL sign language
corpus, we increase the number of confident automatic annotations from 670K to
5M. We make these annotations publicly available to support the sign language
research community.
