# iLSU-T: an Open Dataset for Uruguayan Sign Language Translation

arXiv: https://arxiv.org/abs/2507.21104 · HTML: https://mlsanity.com/paper/2507.21104/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 48 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 27 of 27)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://mlsanity.com/paper/2105.12397/) (2021)
- [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://mlsanity.com/paper/2008.08143/) (2020)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective](https://mlsanity.com/paper/1908.08597/) (2019)
- [TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation](https://mlsanity.com/paper/2010.05468/) (2020)
- [Open-Domain Sign Language Translation Learned from Online Video](https://mlsanity.com/paper/2205.12870/) (2022)
- [Neural Sign Language Translation based on Human Keypoint Estimation](https://mlsanity.com/paper/1811.11436/) (2018)
- [Transferring Cross-domain Knowledge for Video Sign Language Recognition](https://mlsanity.com/paper/2003.03703/) (2020)
- [Gloss Attention for Gloss-free Sign Language Translation](https://mlsanity.com/paper/2307.07361/) (2023)
- [Sign Language Translation from Instructional Videos](https://mlsanity.com/paper/2304.06371/) (2023)
- [Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation](https://mlsanity.com/paper/2109.13318/) (2021)
- [Read and Attend: Temporal Localisation in Sign Language Videos](https://mlsanity.com/paper/2103.16481/) (2021)
- [Gloss-Free End-to-End Sign Language Translation](https://mlsanity.com/paper/2305.12876/) (2023)
- [Real-Time Sign Language Detection using Human Pose Estimation](https://mlsanity.com/paper/2008.04637/) (2020)
- [BBC-Oxford British Sign Language Dataset](https://mlsanity.com/paper/2111.03635/) (2021)
- [Aligning Subtitles in Sign Language Videos](https://mlsanity.com/paper/2105.02877/) (2021)
- [Machine Translation from Signed to Spoken Languages: State of the Art and Challenges](https://mlsanity.com/paper/2202.03086/) (2022)
- [Sign language segmentation with temporal convolutional networks](https://mlsanity.com/paper/2011.12986/) (2020)
- [Linguistically Motivated Sign Language Segmentation](https://mlsanity.com/paper/2310.13960/) (2023)
- [Fingerspelling Detection in American Sign Language](https://mlsanity.com/paper/2104.01291/) (2021)
- [LSA-T: The first continuous Argentinian Sign Language dataset for Sign Language Translation](https://mlsanity.com/paper/2211.15481/) (2022)
- [A Chinese Continuous Sign Language Dataset Based on Complex Environments](https://mlsanity.com/paper/2409.11960/) (2024)

## Abstract (arXiv)

Automatic sign language translation has gained particular interest in the computer vision and computational linguistics communities in recent years. Given each sign language country particularities, machine translation requires local data to develop new techniques and adapt existing ones. This work presents iLSU T, an open dataset of interpreted Uruguayan Sign Language RGB videos with audio and text transcriptions. This type of multimodal and curated data is paramount for developing novel approaches to understand or generate tools for sign language processing. iLSU T comprises more than 185 hours of interpreted sign language videos from public TV broadcasting. It covers diverse topics and includes the participation of 18 professional interpreters of sign language. A series of experiments using three state of the art translation algorithms is presented. The aim is to establish a baseline for this dataset and evaluate its usefulness and the proposed pipeline for data processing. The experiments highlight the need for more localized datasets for sign language translation and understanding, which are critical for developing novel tools to improve accessibility and inclusion of all individuals. Our data and code can be accessed.