# The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge

arXiv: https://arxiv.org/abs/2411.03568 · HTML: https://mlsanity.com/paper/2411.03568/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 62 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Builds on (most cited first, 18 of 18)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [Two-Stream Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2211.01367/) (2022)
- [BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues](https://mlsanity.com/paper/2007.12131/) (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)
- [YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus](https://mlsanity.com/paper/2306.15162/) (2023)
- [Including Signed Languages in Natural Language Processing](https://mlsanity.com/paper/2105.05222/) (2021)
- [Empath: Understanding Topic Signals in Large-Scale Text](https://mlsanity.com/paper/1602.06979/) (2016)
- [Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review](https://mlsanity.com/paper/2202.12205/) (2022)
- [Considerations for meaningful sign language machine translation based on glosses](https://mlsanity.com/paper/2211.15464/) (2022)
- [Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendas](https://mlsanity.com/paper/2403.02563/) (2024)
- [The Sem-Lex Benchmark: Modeling ASL Signs and Their Phonemes](https://mlsanity.com/paper/2310.00196/) (2023)
- [Neuro-symbolic Architectures for Context Understanding](https://mlsanity.com/paper/2003.04707/) (2020)
- [Exploring Strategies for Modeling Sign Language Phonology](https://mlsanity.com/paper/2310.00195/) (2023)
- [3D-LEX v1.0: 3D Lexicons for American Sign Language and Sign Language of the Netherlands](https://mlsanity.com/paper/2409.01901/) (2024)

## Built on by (most cited first, 1 of 1)

- [Targeted Linguistic Analysis of Sign Language Models with Minimal Translation Pairs](https://mlsanity.com/paper/2604.27232/) (2026)

## Abstract (arXiv)

Language models for American Sign Language (ASL) could make language technologies substantially more accessible to those who sign. To train models on tasks such as isolated sign recognition (ISR) and ASL-to-English translation, datasets provide annotated video examples of ASL signs. To facilitate the generalizability and explainability of these models, we introduce the American Sign Language Knowledge Graph (ASLKG), compiled from twelve sources of expert linguistic knowledge. We use the ASLKG to train neuro-symbolic models for 3 ASL understanding tasks, achieving accuracies of 91% on ISR, 14% for predicting the semantic features of unseen signs, and 36% for classifying the topic of Youtube-ASL videos.