Including Signed Languages in Natural Language Processing

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pages 7347–7360 August 1–6, 2021. ©2021 Association for Computational Linguistics 7347 Including Signed Languages in Natural Language Processing Kayo Yin1, Amit Moryossef2, Julie Hochgesang3, Yoav Goldberg2,4, Malihe Alikhani5 1Language Technologies Institute, Carnegie Mellon University 2Bar-Ilan University 3Department of Linguistics, Gallaudet University 4Allen Institute for AI 5School of Computing and Information, University of Pittsburgh kayoy@cs.cmu.edu, amitmoryossef@gmail.com julie.hochgesang@gallaudet.edu, yogo@cs.biu.ac.il, malihe@pitt.edu 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 proper- ties of natural language, we believe that tools and theories of Natural Language Processing (NLP) are crucial towards its modeling. How- ever, existing research in Sign Language Pro- cessing (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 sci- entific impact. We first discuss the linguistic properties of signed languages to consider dur- ing their modeling. Then, we review the limi- tations of current SLP models and identify the open challenges to extend NLP to signed lan- guages. Finally, we urge (1) the adoption of an efficient tokenization method; (2) the devel- opment 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. 1 Introduction Natural Language Processing (NLP) has revolu- tionized the way people interact with technology through the rise of personal assistants and machine translation systems, to name a few. However, the vast majority of NLP models require a spoken lan- guage input (speech or text), thereby excluding around 200 different signed languages and up to 70 million deaf people1 from modern language tech- nologies. 1According to World Federation of the Deaf https://wfdeaf.org/our-work/ 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 Year 0 100 200 300 400 # of publications All Computer Science ACL Anthology Figure 1: Evolution of the number of publications refer- ring to sign language in its title from computer science venues and in the ACL anthology. Publications in com- puter science are extracted from the Semantic Scholar archive (Ammar et al., 2018). Throughout history, Deaf communities fought for the right to learn and use signed languages, as well as for the recognition of signed languages as le- gitimate languages (§2). Indeed, signed languages are sophisticated communication modalities that are at least as capable as spoken languages in all manners, linguistic and social. However, in a pre- dominantly oral society, deaf people are constantly encouraged to use spoken languages through lip- reading or text-based communication. The exclu- sion of signed languages from modern language technologies further suppresses signing in favor of spoken languages. This disregards the preferences of the Deaf communities who strongly prefer to communicate in signed languages both online and …

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