  Signed Language Processing (SLP) concerns the automated processing of signed
languages, the main means of communication of Deaf and hearing impaired
individuals. SLP features many different tasks, ranging from sign recognition
to translation and production of signed speech, but has been overlooked by the
NLP community thus far. In this paper, we bring to attention the task of
modelling the phonology of sign languages. We leverage existing resources to
construct a large-scale dataset of American Sign Language signs annotated with
six different phonological properties. We then conduct an extensive empirical
study to investigate whether data-driven end-to-end and feature-based
approaches can be optimised to automatically recognise these properties. We
find that, despite the inherent challenges of the task, graph-based neural
networks that operate over skeleton features extracted from raw videos are able
to succeed at the task to a varying degree. Most importantly, we show that this
performance pertains even on signs unobserved during training.
