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Automatic Sign Language Translation (SLT) is a research avenue of greatsocietal impact. End-to-End SLT facilitates the interaction of Hard-of-Hearing(HoH) with hearing people, thus improving their social life and opportunitiesfor participation in social life. However, research within this frame ofreference is still in its infancy, and current resources are particularlylimited. Existing SLT methods are either of low translation ability or aretrained and evaluated on datasets of restricted vocabulary and questionablereal-world value. A characteristic example is Phoenix2014T benchmark dataset,which only covers weather forecasts in German Sign Language. To address thisshortage of resources, we introduce a newly constructed collection of 29653Greek Sign Language video-translation pairs which is based on the officialsyllabus of Greek Elementary School. Our dataset covers a wide range ofsubjects. We use this novel dataset to train recent state-of-the-artTransformer-based methods widely used in SLT research. Our results demonstratethe potential of our introduced dataset to advance SLT research by offering afavourable balance between usability and real-world value.

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