Summary of From Rule-based Models to Deep Learning Transformers Architectures For Natural Language Processing and Sign Language Translation Systems: Survey, Taxonomy and Performance Evaluation, by Nada Shahin and Leila Ismail
From Rule-Based Models to Deep Learning Transformers Architectures for Natural Language Processing and Sign Language Translation Systems: Survey, Taxonomy and Performance Evaluation
by Nada Shahin, Leila Ismail
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Sign language machine translation is crucial to cater to the growing Deaf and Hard of Hearing population worldwide, but there’s a shortage of certified interpreters. The paper fills this void by analyzing the evolution of sign language machine translation algorithms and proposing future directions. It also highlights the requirements for an integrated end-to-end system from sign to gloss to text and vice-versa. Transformers architectures are discussed as a prominent approach in language translation, while deep learning algorithms are emphasized for accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sign languages are unique because they’re continuous and dynamic. To help people who are Deaf or Hard of Hearing, we need a way to translate signs into words quickly and accurately. This paper looks at how machines can do this kind of translation, and what’s needed to make it happen. It also talks about the importance of deep learning algorithms for getting the job done right. |
Keywords
» Artificial intelligence » Deep learning » Translation
