Towards A Sign Language Gloss Representation Of Modern Standard Arabic
This work addresses the need for translation models for deaf individuals who use sign language, specifically targeting Modern Standard Arabic, but it appears incremental as it builds on existing gloss representation methods.
The paper tackled the problem of translating Modern Standard Arabic into sign language by generating a gloss representation that extracts features necessary for animation sign generation, focusing on maintaining the meaning of input Arabic sentences.
Over 5% of the world's population (466 million people) has disabling hearing loss. 4 million are children. They can be hard of hearing or deaf. Deaf people mostly have profound hearing loss. Which implies very little or no hearing. Over the world, deaf people often communicate using a sign language with gestures of both hands and facial expressions. The sign language is a full-fledged natural language with its own grammar and lexicon. Therefore, there is a need for translation models from and to sign languages. In this work, we are interested in the translation of Modern Standard Arabic(MSAr) into sign language. We generated a gloss representation from MSAr that extracts the features mandatory for the generation of animation signs. Our approach locates the most pertinent features that maintain the meaning of the input Arabic sentence.