# WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language

arXiv: https://arxiv.org/abs/2203.06096 · HTML: https://mlsanity.com/paper/2203.06096/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 11 Mar 2022.

## Builds on (most cited first, 12 of 12)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration](https://mlsanity.com/paper/2108.06428/) (2021)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task](https://mlsanity.com/paper/1606.02858/) (2016)
- [Including Signed Languages in Natural Language Processing](https://mlsanity.com/paper/2105.05222/) (2021)
- [Quantitative Survey of the State of the Art in Sign Language Recognition](https://mlsanity.com/paper/2008.09918/) (2020)
- [Real-Time Sign Language Detection using Human Pose Estimation](https://mlsanity.com/paper/2008.04637/) (2020)
- [A Framework for Evaluation of Machine Reading Comprehension Gold Standards](https://mlsanity.com/paper/2003.04642/) (2020)
- [Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble](https://mlsanity.com/paper/2110.06161/) (2021)
- [Phonology Recognition in American Sign Language](https://mlsanity.com/paper/2110.00453/) (2021)

## Built on by (most cited first, 6 of 6)

- [The Sem-Lex Benchmark: Modeling ASL Signs and Their Phonemes](https://mlsanity.com/paper/2310.00196/) (2023)
- [Self-Supervised Video Transformers for Isolated Sign Language Recognition](https://mlsanity.com/paper/2309.02450/) (2023)
- [Exploring Strategies for Modeling Sign Language Phonology](https://mlsanity.com/paper/2310.00195/) (2023)
- [Improving Sign Recognition with Phonology](https://mlsanity.com/paper/2302.05759/) (2023)
- [Bridging the Communication Gap: Artificial Agents Learning Sign Language through Imitation](https://mlsanity.com/paper/2406.10043/) (2024)
- [A Hybrid Kinematic GCN and Elastic Transformer for Efficient Indian Sign Language Recognition](https://mlsanity.com/paper/2610.14680/) (2026)

## Abstract (arXiv)

Signed Language Processing (SLP) concerns the automated processing of signed languages, the main means of communication of Deaf and hearing impaired individuals. SLP features many different tasks, ranging from sign recognition to translation and production of signed speech, but has been overlooked by the NLP community thus far. In this paper, we bring to attention the task of modelling the phonology of sign languages. We leverage existing resources to construct a large-scale dataset of American Sign Language signs annotated with six different phonological properties. We then conduct an extensive empirical study to investigate whether data-driven end-to-end and feature-based approaches can be optimised to automatically recognise these properties. We find that, despite the inherent challenges of the task, graph-based neural networks that operate over skeleton features extracted from raw videos are able to succeed at the task to a varying degree. Most importantly, we show that this performance pertains even on signs unobserved during training.