@inproceedings{yuan-etal-2024-translation,
title = "Translation Quality Evaluation of Sign Language Avatar",
author = "Yuan, Zhao and
Ruiquan, Zhang and
Dengfeng, Yao and
Yidong, Chen",
editor = "Lin, Hongfei and
Tan, Hongye and
Li, Bin",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-3.45/",
pages = "405--415",
language = "eng",
abstract = "{\textquotedblleft}Sign Language Avatar technology aims to create virtual agents capable of communicating with deaf individuals through sign language, similar to the text dialogue agent ChatGPT but focusing on sign language communication. Challenges in sign language production include limited dataset sizes, information loss due to reliance on intermediate representations, and insufficient realism in generated actions. In this event, we particularly focus on the ability of the Sign Language Avatar to translate spoken language text into sign language that is easily understood by deaf individuals. As the first sign language avatar event held by the China National Conference on Computational Linguistics(CCL), this event attracted wide attention from both industry and academia, with 14 teams registering and 10 of them submitting their system interfaces on time. We provided a dataset consisting of 1074 text-video parallel sentence pairs for training, and the evaluation team comprised proficient Chinese sign language users and professional sign language translators. The scoring method employed a comprehensive evaluation based on multiple metrics, focusing primarily on sign language grammar accuracy, naturalness, readability, and cultural adaptability. The final scores were determined by considering performance across these four aspects. The final scores, taking into account these four aspects, showed that four teams demonstrated good readability, with Vivo Mobile Communication Co., Ltd. ranking first with a score of 3.513 (out of a full score of 5), leading the baseline model by 1.394 points. According to the analysis of the results, most teams used the traditional method of converting text into Gloss sequences before generating sign language. Additionally, some teams experimented with emerging methods, including gloss-free end-to-end training and Large Language Model(LLMs) prompt learning, which also achieved promising results. We anticipate that this event will promote the development of sign language avatar technology and provide higher-quality communication tools for the deaf community. For more information on this task, please visit the website of the CCL24-Eval: Translation Quality Evaluation of Sign Language Avatar Task.{\textquotedblright}"
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yuan-etal-2024-translation">
<titleInfo>
<title>Translation Quality Evaluation of Sign Language Avatar</title>
</titleInfo>
<name type="personal">
<namePart type="given">Zhao</namePart>
<namePart type="family">Yuan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhang</namePart>
<namePart type="family">Ruiquan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yao</namePart>
<namePart type="family">Dengfeng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chen</namePart>
<namePart type="family">Yidong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<language>
<languageTerm type="text">eng</languageTerm>
</language>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hongfei</namePart>
<namePart type="family">Lin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hongye</namePart>
<namePart type="family">Tan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bin</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Chinese Information Processing Society of China</publisher>
<place>
<placeTerm type="text">Taiyuan, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>“Sign Language Avatar technology aims to create virtual agents capable of communicating with deaf individuals through sign language, similar to the text dialogue agent ChatGPT but focusing on sign language communication. Challenges in sign language production include limited dataset sizes, information loss due to reliance on intermediate representations, and insufficient realism in generated actions. In this event, we particularly focus on the ability of the Sign Language Avatar to translate spoken language text into sign language that is easily understood by deaf individuals. As the first sign language avatar event held by the China National Conference on Computational Linguistics(CCL), this event attracted wide attention from both industry and academia, with 14 teams registering and 10 of them submitting their system interfaces on time. We provided a dataset consisting of 1074 text-video parallel sentence pairs for training, and the evaluation team comprised proficient Chinese sign language users and professional sign language translators. The scoring method employed a comprehensive evaluation based on multiple metrics, focusing primarily on sign language grammar accuracy, naturalness, readability, and cultural adaptability. The final scores were determined by considering performance across these four aspects. The final scores, taking into account these four aspects, showed that four teams demonstrated good readability, with Vivo Mobile Communication Co., Ltd. ranking first with a score of 3.513 (out of a full score of 5), leading the baseline model by 1.394 points. According to the analysis of the results, most teams used the traditional method of converting text into Gloss sequences before generating sign language. Additionally, some teams experimented with emerging methods, including gloss-free end-to-end training and Large Language Model(LLMs) prompt learning, which also achieved promising results. We anticipate that this event will promote the development of sign language avatar technology and provide higher-quality communication tools for the deaf community. For more information on this task, please visit the website of the CCL24-Eval: Translation Quality Evaluation of Sign Language Avatar Task.”</abstract>
<identifier type="citekey">yuan-etal-2024-translation</identifier>
<location>
<url>https://aclanthology.org/2024.ccl-3.45/</url>
</location>
<part>
<date>2024-07</date>
<extent unit="page">
<start>405</start>
<end>415</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Translation Quality Evaluation of Sign Language Avatar
%A Yuan, Zhao
%A Ruiquan, Zhang
%A Dengfeng, Yao
%A Yidong, Chen
%Y Lin, Hongfei
%Y Tan, Hongye
%Y Li, Bin
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F yuan-etal-2024-translation
%X “Sign Language Avatar technology aims to create virtual agents capable of communicating with deaf individuals through sign language, similar to the text dialogue agent ChatGPT but focusing on sign language communication. Challenges in sign language production include limited dataset sizes, information loss due to reliance on intermediate representations, and insufficient realism in generated actions. In this event, we particularly focus on the ability of the Sign Language Avatar to translate spoken language text into sign language that is easily understood by deaf individuals. As the first sign language avatar event held by the China National Conference on Computational Linguistics(CCL), this event attracted wide attention from both industry and academia, with 14 teams registering and 10 of them submitting their system interfaces on time. We provided a dataset consisting of 1074 text-video parallel sentence pairs for training, and the evaluation team comprised proficient Chinese sign language users and professional sign language translators. The scoring method employed a comprehensive evaluation based on multiple metrics, focusing primarily on sign language grammar accuracy, naturalness, readability, and cultural adaptability. The final scores were determined by considering performance across these four aspects. The final scores, taking into account these four aspects, showed that four teams demonstrated good readability, with Vivo Mobile Communication Co., Ltd. ranking first with a score of 3.513 (out of a full score of 5), leading the baseline model by 1.394 points. According to the analysis of the results, most teams used the traditional method of converting text into Gloss sequences before generating sign language. Additionally, some teams experimented with emerging methods, including gloss-free end-to-end training and Large Language Model(LLMs) prompt learning, which also achieved promising results. We anticipate that this event will promote the development of sign language avatar technology and provide higher-quality communication tools for the deaf community. For more information on this task, please visit the website of the CCL24-Eval: Translation Quality Evaluation of Sign Language Avatar Task.”
%U https://aclanthology.org/2024.ccl-3.45/
%P 405-415
Markdown (Informal)
[Translation Quality Evaluation of Sign Language Avatar](https://aclanthology.org/2024.ccl-3.45/) (Yuan et al., CCL 2024)
ACL
- Zhao Yuan, Zhang Ruiquan, Yao Dengfeng, and Chen Yidong. 2024. Translation Quality Evaluation of Sign Language Avatar. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 405–415, Taiyuan, China. Chinese Information Processing Society of China.