# From Rule-Based Models to Deep Learning Transformers Architectures for Natural Language Processing and Sign Language Translation Systems: Survey, Taxonomy and Performance Evaluation

arXiv: https://arxiv.org/abs/2408.14825 · HTML: https://mlsanity.com/paper/2408.14825/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 65 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Neural Machine Translation: A Review and Survey](https://mlsanity.com/paper/1912.02047/) (2019)
- [Video-based Sign Language Recognition without Temporal Segmentation](https://mlsanity.com/paper/1801.10111/) (2018)
- [Neural Sign Language Translation based on Human Keypoint Estimation](https://mlsanity.com/paper/1811.11436/) (2018)
- [Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation](https://mlsanity.com/paper/2205.15301/) (2022)
- [SimulSLT: End-to-End Simultaneous Sign Language Translation](https://mlsanity.com/paper/2112.04228/) (2021)
- [DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation](https://mlsanity.com/paper/1802.07584/) (2018)
- [Neural-based machine translation for medical text domain. Based on European Medicines Agency leaflet texts](https://mlsanity.com/paper/1509.08644/) (2015)
- [QoS-SLA-Aware Adaptive Genetic Algorithm for Multi-Request Offloading in Integrated Edge-Cloud Computing in Internet of Vehicles](https://mlsanity.com/paper/2202.01696/) (2022)

## Built on by (most cited first, 6 of 6)

- [GLoT: A Novel Gated-Logarithmic Transformer for Efficient Sign Language Translation](https://mlsanity.com/paper/2502.12223/) (2025)
- [ADAT: Time-Series-Aware Adaptive Transformer Architecture for Sign Language Translation](https://mlsanity.com/paper/2504.11942/) (2025)
- [Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational Videos](https://mlsanity.com/paper/2505.01790/) (2025)
- [EASL: Multi-Emotion Guided Semantic Disentanglement for Expressive Sign Language Generation](https://mlsanity.com/paper/2511.22135/) (2025)
- [HATL: Hierarchical Adaptive-Transfer Learning Framework for Sign Language Machine Translation](https://mlsanity.com/paper/2603.19260/) (2026)
- [Gloss-Free Representation Learning for Cross-Dataset Sign Spotting](https://mlsanity.com/paper/2608.11332/) (2026)

## Abstract (arXiv)

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 ma-chine translation system underpinned by accurate deep learning algorithms. We propose future research directions for sign language translation systems.