"随着对手语进行大规模数据化处理的需求日益增强,手语的音系学标注及规范化工作愈发迫切。然而,手语作为一种视觉-空间语言,不同于有声语言,其多信道(手形、位置、手掌朝向、运动方式以及面部表情、躯干动作等非手动特征)信息的复杂性与缺乏统一标注规范,一直制约着手语语料库构建与自动分析技术的发展。针对这一问题,本研究在手语音系学理论的指导下,提出了一套面向中国手语音系学标注加工的系统化规范。该规范由原则和细则两部分构成:原则部分明确标注对象的粒度、标注单位的界定与分层方式;细则部分则给出多信道特征的具体标注实例与操作指南。该规范的实施为中国手语多信道特征的系统标注提供基础支撑,将有助于推动手语识别、翻译、生成以及教学平台的深入发展,加速中国手语信息处理标准化与规范化的进程。"

“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.”