# LSA-T: The first continuous Argentinian Sign Language dataset for Sign Language Translation

arXiv: https://arxiv.org/abs/2211.15481 · HTML: https://mlsanity.com/paper/2211.15481/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [Video-based Sign Language Recognition without Temporal Segmentation](https://mlsanity.com/paper/1801.10111/) (2018)
- [Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective](https://mlsanity.com/paper/1908.08597/) (2019)
- [Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition](https://mlsanity.com/paper/2002.03187/) (2020)
- [Neural Sign Language Translation based on Human Keypoint Estimation](https://mlsanity.com/paper/1811.11436/) (2018)
- [Quantitative Survey of the State of the Art in Sign Language Recognition](https://mlsanity.com/paper/2008.09918/) (2020)
- [A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition](https://mlsanity.com/paper/2007.12530/) (2020)
- [Keypoint based Sign Language Translation without Glosses](https://mlsanity.com/paper/2204.10511/) (2022)

## Built on by (most cited first, 4 of 4)

- [Human Motion Video Generation: A Survey](https://mlsanity.com/paper/2509.03883/) (2025)
- [A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities](https://mlsanity.com/paper/2408.05436/) (2024)
- [iLSU-T: an Open Dataset for Uruguayan Sign Language Translation](https://mlsanity.com/paper/2507.21104/) (2025)
- [Target-Side Paraphrase Augmentation for Sign Language Translation with Large Language Models](https://mlsanity.com/paper/2605.31393/) (2026)

## Abstract (arXiv)

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.