From rule-based models to deep learning transformers architectures for natural language processing and sign language translation systems: survey, taxonomy and performance evaluation

• Published in: The Artificial intelligence review Vol. 57; no. 10; p. 271
• Main Authors: Shahin, Nada, Ismail, Leila
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 29.08.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Communication; Computer Science; Deafness; Deep learning; Evolutionary algorithms; Gloss; Hearing loss; Interpreters; Language translation; Machine learning; Machine translation; Natural language processing; Neural networks; Performance evaluation; Real time; Sign language; Syntax; Taxonomy; Transformers; Deep learning; Sign language translation; Transformers; Natural language processing; Artificial intelligence; Neural machine translation
• ISSN: 1573-7462; 0269-2821; 1573-7462
• DOI: 10.1007/s10462-024-10895-z

With the growing Deaf and Hard of Hearing population worldwide and the persistent shortage of certified sign language interpreters, there is a pressing need for an efficient, signs-driven, integrated end-to-end translation system, from sign to gloss to text and vice-versa. There has been a wealth of research on machine translations and related reviews. However, there are few works on sign language machine translation considering the particularity of the language being continuous and dynamic. This paper aims to address this void, providing a retrospective analysis of the temporal evolution of sign language machine translation algorithms and a taxonomy of the Transformers architectures, the most used approach in language translation. We also present the requirements of a real-time Quality-of-Service sign language machine translation system underpinned by accurate deep learning algorithms. We propose future research directions for sign language translation systems.