Dr. Malihe Alikhani (Assistant Professor, CS) was awarded the best theme paper at the Annual Meeting of the Association for Computational Linguistics (ACL 2021) for her paper "Including Signed Languages in Natural Language Processing." Dr. Alikhani worked with Dr. Kayo Yin from the Language Technologies Institute at Carnegie Mellon University, Dr. Amit Moryossef from Bar-Ilan University, Dr. Julie Hochgesang from the Department of Linguistics at Gallaudet University, and Dr. Yoav Goldberg from the Allen Institute for AI.
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 attempts 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. Read more here.