# A New Dataset for End-to-End Sign Language Translation: The Greek Elementary School Dataset

arXiv: https://arxiv.org/abs/2310.04753 · HTML: https://mlsanity.com/paper/2310.04753/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 45 references that are in the map.
- First posted on arXiv 7 Oct 2023.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://mlsanity.com/paper/2105.12397/) (2021)
- [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://mlsanity.com/paper/2008.08143/) (2020)
- [Open-Domain Sign Language Translation Learned from Online Video](https://mlsanity.com/paper/2205.12870/) (2022)
- [Multi-channel Transformers for Multi-articulatory Sign Language Translation](https://mlsanity.com/paper/2009.00299/) (2020)
- [Joey NMT: A Minimalist NMT Toolkit for Novices](https://mlsanity.com/paper/1907.12484/) (2019)
- [Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation](https://mlsanity.com/paper/2109.13318/) (2021)
- [Content4All Open Research Sign Language Translation Datasets](https://mlsanity.com/paper/2105.02351/) (2021)
- [BBC-Oxford British Sign Language Dataset](https://mlsanity.com/paper/2111.03635/) (2021)
- [BosphorusSign22k Sign Language Recognition Dataset](https://mlsanity.com/paper/2004.01283/) (2020)
- [Nonparametric Bayesian Deep Networks with Local Competition](https://mlsanity.com/paper/1805.07624/) (2018)
- [A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition](https://mlsanity.com/paper/2007.12530/) (2020)
- [Local Competition and Stochasticity for Adversarial Robustness in Deep Learning](https://mlsanity.com/paper/2101.01121/) (2021)
- [PiSLTRc: Position-informed Sign Language Transformer with Content-aware Convolution](https://mlsanity.com/paper/2107.12600/) (2021)
- [Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness](https://mlsanity.com/paper/2112.02671/) (2021)
- [Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning](https://mlsanity.com/paper/2208.01573/) (2022)
- [Competing Mutual Information Constraints with Stochastic Competition-based Activations for Learning Diversified Representations](https://mlsanity.com/paper/2201.03624/) (2022)

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

- [Scaling Sign Language Translation](https://mlsanity.com/paper/2407.11855/) (2024)
- [Transformers with Stochastic Competition for Tabular Data Modelling](https://mlsanity.com/paper/2407.13238/) (2024)
- [E-TSL: A Continuous Educational Turkish Sign Language Dataset with Baseline Methods](https://mlsanity.com/paper/2405.02984/) (2024)
- [AirLetters: An Open Video Dataset of Characters Drawn in the Air](https://mlsanity.com/paper/2410.02921/) (2024)
- [SkelCap: Automated Generation of Descriptive Text from Skeleton Keypoint Sequences](https://mlsanity.com/paper/2405.02977/) (2024)
- [Pass or Fail? Evaluating LLMs on Two Greek Examination Benchmarks](https://mlsanity.com/paper/2609.34800/) (2026)

## Abstract (arXiv)

Automatic Sign Language Translation (SLT) is a research avenue of great societal impact. End-to-End SLT facilitates the interaction of Hard-of-Hearing (HoH) with hearing people, thus improving their social life and opportunities for participation in social life. However, research within this frame of reference is still in its infancy, and current resources are particularly limited. Existing SLT methods are either of low translation ability or are trained and evaluated on datasets of restricted vocabulary and questionable real-world value. A characteristic example is Phoenix2014T benchmark dataset, which only covers weather forecasts in German Sign Language. To address this shortage of resources, we introduce a newly constructed collection of 29653 Greek Sign Language video-translation pairs which is based on the official syllabus of Greek Elementary School. Our dataset covers a wide range of subjects. We use this novel dataset to train recent state-of-the-art Transformer-based methods widely used in SLT research. Our results demonstrate the potential of our introduced dataset to advance SLT research by offering a favourable balance between usability and real-world value.