@inproceedings{xu-etal-2022-automatic,
title = "Automatic Gloss Dictionary for Sign Language Learners",
author = "Xu, Chenchen and
Li, Dongxu and
Li, Hongdong and
Suominen, Hanna and
Swift, Ben",
editor = "Basile, Valerio and
Kozareva, Zornitsa and
Stajner, Sanja",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-demo.8",
doi = "10.18653/v1/2022.acl-demo.8",
pages = "83--92",
abstract = "A multi-language dictionary is a fundamental tool for language learning, allowing the learner to look up unfamiliar words. Searching an unrecognized word in the dictionary does not usually require deep knowledge of the target language. However, this is not true for sign language, where gestural elements preclude this type of easy lookup. This paper introduces GlossFinder, an online tool supporting 2, 000 signs to assist language learners in determining the meaning of given signs. Unlike alternative systems of complex inputs, our system requires only that learners imitate the sign in front of a standard webcam. A user study conducted among sign language speakers of varying ability compared our system against existing alternatives and the interviews indicated a clear preference for our new system. This implies that GlossFinder can lower the barrier in sign language learning by addressing the common problem of sign finding and make it accessible to the wider community.",
}
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<abstract>A multi-language dictionary is a fundamental tool for language learning, allowing the learner to look up unfamiliar words. Searching an unrecognized word in the dictionary does not usually require deep knowledge of the target language. However, this is not true for sign language, where gestural elements preclude this type of easy lookup. This paper introduces GlossFinder, an online tool supporting 2, 000 signs to assist language learners in determining the meaning of given signs. Unlike alternative systems of complex inputs, our system requires only that learners imitate the sign in front of a standard webcam. A user study conducted among sign language speakers of varying ability compared our system against existing alternatives and the interviews indicated a clear preference for our new system. This implies that GlossFinder can lower the barrier in sign language learning by addressing the common problem of sign finding and make it accessible to the wider community.</abstract>
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%0 Conference Proceedings
%T Automatic Gloss Dictionary for Sign Language Learners
%A Xu, Chenchen
%A Li, Dongxu
%A Li, Hongdong
%A Suominen, Hanna
%A Swift, Ben
%Y Basile, Valerio
%Y Kozareva, Zornitsa
%Y Stajner, Sanja
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F xu-etal-2022-automatic
%X A multi-language dictionary is a fundamental tool for language learning, allowing the learner to look up unfamiliar words. Searching an unrecognized word in the dictionary does not usually require deep knowledge of the target language. However, this is not true for sign language, where gestural elements preclude this type of easy lookup. This paper introduces GlossFinder, an online tool supporting 2, 000 signs to assist language learners in determining the meaning of given signs. Unlike alternative systems of complex inputs, our system requires only that learners imitate the sign in front of a standard webcam. A user study conducted among sign language speakers of varying ability compared our system against existing alternatives and the interviews indicated a clear preference for our new system. This implies that GlossFinder can lower the barrier in sign language learning by addressing the common problem of sign finding and make it accessible to the wider community.
%R 10.18653/v1/2022.acl-demo.8
%U https://aclanthology.org/2022.acl-demo.8
%U https://doi.org/10.18653/v1/2022.acl-demo.8
%P 83-92
Markdown (Informal)
[Automatic Gloss Dictionary for Sign Language Learners](https://aclanthology.org/2022.acl-demo.8) (Xu et al., ACL 2022)
ACL
- Chenchen Xu, Dongxu Li, Hongdong Li, Hanna Suominen, and Ben Swift. 2022. Automatic Gloss Dictionary for Sign Language Learners. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 83–92, Dublin, Ireland. Association for Computational Linguistics.