Searching for fingerspelled content in American Sign Language

Bowen Shi, Diane Brentari, Greg Shakhnarovich, Karen Livescu


Abstract
Natural language processing for sign language video—including tasks like recognition, translation, and search—is crucial for making artificial intelligence technologies accessible to deaf individuals, and is gaining research interest in recent years. In this paper, we address the problem of searching for fingerspelled keywords or key phrases in raw sign language videos. This is an important task since significant content in sign language is often conveyed via fingerspelling, and to our knowledge the task has not been studied before. We propose an end-to-end model for this task, FSS-Net, that jointly detects fingerspelling and matches it to a text sequence. Our experiments, done on a large public dataset of ASL fingerspelling in the wild, show the importance of fingerspelling detection as a component of a search and retrieval model. Our model significantly outperforms baseline methods adapted from prior work on related tasks.
Anthology ID:
2022.acl-long.119
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1699–1712
Language:
URL:
https://aclanthology.org/2022.acl-long.119
DOI:
10.18653/v1/2022.acl-long.119
Bibkey:
Cite (ACL):
Bowen Shi, Diane Brentari, Greg Shakhnarovich, and Karen Livescu. 2022. Searching for fingerspelled content in American Sign Language. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1699–1712, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Searching for fingerspelled content in American Sign Language (Shi et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.119.pdf