  This study introduces the continuous Educational Turkish Sign Language
(E-TSL) dataset, collected from online Turkish language lessons for 5th, 6th,
and 8th grades. The dataset comprises 1,410 videos totaling nearly 24 hours and
includes performances from 11 signers. Turkish, an agglutinative language,
poses unique challenges for sign language translation, particularly with a
vocabulary where 64% are singleton words and 85% are rare words, appearing less
than five times. We developed two baseline models to address these challenges:
the Pose to Text Transformer (P2T-T) and the Graph Neural Network based
Transformer (GNN-T) models. The GNN-T model achieved 19.13% BLEU-1 score and
3.28% BLEU-4 score, presenting a significant challenge compared to existing
benchmarks. The P2T-T model, while demonstrating slightly lower performance in
BLEU scores, achieved a higher ROUGE-L score of 22.09%. Additionally, we
benchmarked our model using the well-known PHOENIX-Weather 2014T dataset to
validate our approach.
