  Sign language segmentation is a crucial task in sign language processing
systems. It enables downstream tasks such as sign recognition, transcription,
and machine translation. In this work, we consider two kinds of segmentation:
segmentation into individual signs and segmentation into phrases, larger units
comprising several signs. We propose a novel approach to jointly model these
two tasks.
  Our method is motivated by linguistic cues observed in sign language corpora.
We replace the predominant IO tagging scheme with BIO tagging to account for
continuous signing. Given that prosody plays a significant role in phrase
boundaries, we explore the use of optical flow features. We also provide an
extensive analysis of hand shapes and 3D hand normalization.
  We find that introducing BIO tagging is necessary to model sign boundaries.
Explicitly encoding prosody by optical flow improves segmentation in shallow
models, but its contribution is negligible in deeper models. Careful tuning of
the decoding algorithm atop the models further improves the segmentation
quality.
  We demonstrate that our final models generalize to out-of-domain video
content in a different signed language, even under a zero-shot setting. We
observe that including optical flow and 3D hand normalization enhances the
robustness of the model in this context.
