@inproceedings{yu-etal-2024-action-concentrated,
title = "Action-Concentrated Embedding Framework: This Is Your Captain Sign-tokening",
author = "Yu, Hyunwook and
Shin, Suhyeon and
Heo, Junku and
Shin, Hyuntaek and
Kim, Hyosu and
Kim, Mucheol",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.29",
pages = "310--320",
abstract = "Sign language is the primary communication medium for people who are deaf or have hearing loss. However, given the divergent range of sensory abilities of these individuals, there is a communication gap that needs to be addressed. In this paper, we present action-concentrated embedding (ACE), which is a novel sign token embedding framework. Additionally, to provide a more structured foundation for sign language analysis, we introduce a dedicated notation system tailored for sign language that endeavors to encapsulate the nuanced gestures and movements that are integral with sign communication. The proposed ACE approach tracks a signer{'}s actions based on human posture estimation. Tokenizing these actions and capturing the token embedding using a short-time Fourier transform encapsulates the time-based behavioral changes. Hence, ACE offers input embedding to translate sign language into natural language sentences. When tested against a disaster sign language dataset using automated machine translation measures, ACE notably surpasses prior research in terms of translation capabilities, improving the performance by up to 5.79{\%} for BLEU-4 and 5.46{\%} for ROUGE-L metric.",
}
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<abstract>Sign language is the primary communication medium for people who are deaf or have hearing loss. However, given the divergent range of sensory abilities of these individuals, there is a communication gap that needs to be addressed. In this paper, we present action-concentrated embedding (ACE), which is a novel sign token embedding framework. Additionally, to provide a more structured foundation for sign language analysis, we introduce a dedicated notation system tailored for sign language that endeavors to encapsulate the nuanced gestures and movements that are integral with sign communication. The proposed ACE approach tracks a signer’s actions based on human posture estimation. Tokenizing these actions and capturing the token embedding using a short-time Fourier transform encapsulates the time-based behavioral changes. Hence, ACE offers input embedding to translate sign language into natural language sentences. When tested against a disaster sign language dataset using automated machine translation measures, ACE notably surpasses prior research in terms of translation capabilities, improving the performance by up to 5.79% for BLEU-4 and 5.46% for ROUGE-L metric.</abstract>
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%0 Conference Proceedings
%T Action-Concentrated Embedding Framework: This Is Your Captain Sign-tokening
%A Yu, Hyunwook
%A Shin, Suhyeon
%A Heo, Junku
%A Shin, Hyuntaek
%A Kim, Hyosu
%A Kim, Mucheol
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F yu-etal-2024-action-concentrated
%X Sign language is the primary communication medium for people who are deaf or have hearing loss. However, given the divergent range of sensory abilities of these individuals, there is a communication gap that needs to be addressed. In this paper, we present action-concentrated embedding (ACE), which is a novel sign token embedding framework. Additionally, to provide a more structured foundation for sign language analysis, we introduce a dedicated notation system tailored for sign language that endeavors to encapsulate the nuanced gestures and movements that are integral with sign communication. The proposed ACE approach tracks a signer’s actions based on human posture estimation. Tokenizing these actions and capturing the token embedding using a short-time Fourier transform encapsulates the time-based behavioral changes. Hence, ACE offers input embedding to translate sign language into natural language sentences. When tested against a disaster sign language dataset using automated machine translation measures, ACE notably surpasses prior research in terms of translation capabilities, improving the performance by up to 5.79% for BLEU-4 and 5.46% for ROUGE-L metric.
%U https://aclanthology.org/2024.lrec-main.29
%P 310-320
Markdown (Informal)
[Action-Concentrated Embedding Framework: This Is Your Captain Sign-tokening](https://aclanthology.org/2024.lrec-main.29) (Yu et al., LREC-COLING 2024)
ACL