Towards A Sign Language Gloss Representation Of Modern Standard Arabic
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.
Code (2)
Tasks
SentenceTranslationSimilar Papers 제목 키워드 기반
Bridging Sign and Spoken Languages: Pseudo Gloss Generation for Sign Language Translation
Sign Language Translation (SLT) aims to map sign language videos to spoken language text. A common approach relies on gloss annotations as an intermediate representation, decomposing SLT into two sub-tasks: video-to-glos…
In-Context LearningLarge Language ModelSign Language TranslationTranslation+1Improved Word Sense Disambiguation with Enhanced Sense Representations
Current state-of-the-art supervised word sense disambiguation (WSD) systems (such as GlossBERT and bi-encoder model) yield surprisingly good results by purely leveraging pre-trained language models and short dictionary d…
Word Sense DisambiguationConsiderations for meaningful sign language machine translation based on glosses
Automatic sign language processing is gaining popularity in Natural Language Processing (NLP) research (Yin et al., 2021). In machine translation (MT) in particular, sign language translation based on glosses is a promin…
Machine TranslationSign Language TranslationTranslationLeveraging Gloss Knowledge in Neural Word Sense Disambiguation by Hierarchical Co-Attention
The goal of Word Sense Disambiguation (WSD) is to identify the correct meaning of a word in the particular context. Traditional supervised methods only use labeled data (context), while missing rich lexical knowledge suc…
SentenceWord Sense DisambiguationGloss-free Sign Language Translation: Improving from Visual-Language Pretraining
Sign Language Translation (SLT) is a challenging task due to its cross-domain nature, involving the translation of visual-gestural language to text. Many previous methods employ an intermediate representation, i.e., glos…
DecoderGloss-free Sign Language TranslationSelf-Supervised LearningSign Language Recognition+2