  Sign language translation (SLT) is an active field of study that encompasses
human-computer interaction, computer vision, natural language processing and
machine learning. Progress on this field could lead to higher levels of
integration of deaf people. This paper presents, to the best of our knowledge,
the first continuous Argentinian Sign Language (LSA) dataset. It contains
14,880 sentence level videos of LSA extracted from the CN Sordos YouTube
channel with labels and keypoints annotations for each signer. We also present
a method for inferring the active signer, a detailed analysis of the
characteristics of the dataset, a visualization tool to explore the dataset and
a neural SLT model to serve as baseline for future experiments.
