The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910686542561280 |
|---|---|
| author | Kezar, Lee Munikote, Nidhi Zeng, Zian Sehyr, Zed Caselli, Naomi Thomason, Jesse |
| author_facet | Kezar, Lee Munikote, Nidhi Zeng, Zian Sehyr, Zed Caselli, Naomi Thomason, Jesse |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_03568 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge Kezar, Lee Munikote, Nidhi Zeng, Zian Sehyr, Zed Caselli, Naomi Thomason, Jesse Computation and Language Computer Vision and Pattern Recognition I.2.7 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. |
| title | The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge |
| topic | Computation and Language Computer Vision and Pattern Recognition I.2.7 |
| url | https://arxiv.org/abs/2411.03568 |