Including Signed Languages in Natural Language Processing

TL;DR

This paper advocates for integrating signed languages into NLP research, emphasizing linguistic properties, current limitations, and open challenges to improve modeling and data collection for signed languages.

Contribution

It highlights the importance of signed languages in NLP, reviews existing limitations, and proposes key directions for future research and community involvement.

Findings

Signed languages share core linguistic features with natural language.

Current SLP models lack linguistic awareness and comprehensive data.

Community engagement is crucial for advancing signed language NLP.

Abstract

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.