A New Dataset for End-to-End Sign Language Translation: The Greek
  Elementary School Dataset
Andreas Voskou Konstantinos P. Panousis Harris Partaourides Kyriakos Tolias Sotirios Chatzis
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.