@inproceedings{yin-etal-2024-asl,
title = "{ASL} {STEM} {W}iki: Dataset and Benchmark for Interpreting {STEM} Articles",
author = "Yin, Kayo and
Singh, Chinmay and
Minakov, Fyodor O and
Milan, Vanessa and
Daum{\'e} Iii, Hal and
Zhang, Cyril and
Lu, Alex Xijie and
Bragg, Danielle",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.801",
doi = "10.18653/v1/2024.emnlp-main.801",
pages = "14474--14490",
abstract = "Deaf and hard-of-hearing (DHH) students face significant barriers in accessing science, technology, engineering, and mathematics (STEM) education, notably due to the scarcity of STEM resources in signed languages. To help address this, we introduce ASL STEM Wiki: a parallel corpus of 254 Wikipedia articles on STEM topics in English, interpreted into over 300 hours of American Sign Language (ASL). ASL STEM Wiki is the first continuous signing dataset focused on STEM, facilitating the development of AI resources for STEM education in ASL.We identify several use cases of ASL STEM Wiki with human-centered applications. For example, because this dataset highlights the frequent use of fingerspelling for technical concepts, which inhibits DHH students{'} ability to learn,we develop models to identify fingerspelled words{---}which can later be used to query for appropriate ASL signs to suggest to interpreters.",
}
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<abstract>Deaf and hard-of-hearing (DHH) students face significant barriers in accessing science, technology, engineering, and mathematics (STEM) education, notably due to the scarcity of STEM resources in signed languages. To help address this, we introduce ASL STEM Wiki: a parallel corpus of 254 Wikipedia articles on STEM topics in English, interpreted into over 300 hours of American Sign Language (ASL). ASL STEM Wiki is the first continuous signing dataset focused on STEM, facilitating the development of AI resources for STEM education in ASL.We identify several use cases of ASL STEM Wiki with human-centered applications. For example, because this dataset highlights the frequent use of fingerspelling for technical concepts, which inhibits DHH students’ ability to learn,we develop models to identify fingerspelled words—which can later be used to query for appropriate ASL signs to suggest to interpreters.</abstract>
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%0 Conference Proceedings
%T ASL STEM Wiki: Dataset and Benchmark for Interpreting STEM Articles
%A Yin, Kayo
%A Singh, Chinmay
%A Minakov, Fyodor O.
%A Milan, Vanessa
%A Daumé Iii, Hal
%A Zhang, Cyril
%A Lu, Alex Xijie
%A Bragg, Danielle
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F yin-etal-2024-asl
%X Deaf and hard-of-hearing (DHH) students face significant barriers in accessing science, technology, engineering, and mathematics (STEM) education, notably due to the scarcity of STEM resources in signed languages. To help address this, we introduce ASL STEM Wiki: a parallel corpus of 254 Wikipedia articles on STEM topics in English, interpreted into over 300 hours of American Sign Language (ASL). ASL STEM Wiki is the first continuous signing dataset focused on STEM, facilitating the development of AI resources for STEM education in ASL.We identify several use cases of ASL STEM Wiki with human-centered applications. For example, because this dataset highlights the frequent use of fingerspelling for technical concepts, which inhibits DHH students’ ability to learn,we develop models to identify fingerspelled words—which can later be used to query for appropriate ASL signs to suggest to interpreters.
%R 10.18653/v1/2024.emnlp-main.801
%U https://aclanthology.org/2024.emnlp-main.801
%U https://doi.org/10.18653/v1/2024.emnlp-main.801
%P 14474-14490
Markdown (Informal)
[ASL STEM Wiki: Dataset and Benchmark for Interpreting STEM Articles](https://aclanthology.org/2024.emnlp-main.801) (Yin et al., EMNLP 2024)
ACL
- Kayo Yin, Chinmay Singh, Fyodor O Minakov, Vanessa Milan, Hal Daumé Iii, Cyril Zhang, Alex Xijie Lu, and Danielle Bragg. 2024. ASL STEM Wiki: Dataset and Benchmark for Interpreting STEM Articles. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 14474–14490, Miami, Florida, USA. Association for Computational Linguistics.