A New Dataset for End-to-End Sign Language Translation: The Greek Elementary School Dataset
This addresses the problem of limited real-world resources for sign language translation, which is incremental as it provides a new dataset but uses existing methods.
The authors tackled the lack of diverse and realistic datasets for end-to-end sign language translation by introducing a new dataset of 29,653 Greek Sign Language video-translation pairs based on the Greek elementary school syllabus, and they trained Transformer-based methods on it to demonstrate its potential for advancing research.
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.