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.

自动手语翻译是一个具有巨大社会影响的研究领域，通过端到端手语翻译可以促进听力有困难的人与听力正常的人之间的交流，从而提高他们的社交生活和参与社会生活的机会。针对现有资源有限的问题，研究者构建了一个新的希腊手语视频翻译数据集，包含 29653 个视频翻译对，基于希腊小学的官方课程，该数据集覆盖了广泛的主题，并使用该数据集训练了最新的基于 Transformer 的方法，结果表明该数据集在提供可用性和实际价值方面具有潜力推动 SLT 研究。