  Signed languages are the primary means of communication for many deaf and
hard of hearing individuals. Since signed languages exhibit all the fundamental
linguistic properties of natural language, we believe that tools and theories
of Natural Language Processing (NLP) are crucial towards its modeling. However,
existing research in Sign Language Processing (SLP) seldom attempt to explore
and leverage the linguistic organization of signed languages. This position
paper calls on the NLP community to include signed languages as a research area
with high social and scientific impact. We first discuss the linguistic
properties of signed languages to consider during their modeling. Then, we
review the limitations of current SLP models and identify the open challenges
to extend NLP to signed languages. Finally, we urge (1) the adoption of an
efficient tokenization method; (2) the development of linguistically-informed
models; (3) the collection of real-world signed language data; (4) the
inclusion of local signed language communities as an active and leading voice
in the direction of research.
