构建手语知识图谱，提升手语模型的准确性与可解释性。
The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge
- 整合12个专家语言学来源，构建美国手语知识图谱。
- 在孤立手语识别任务中达到91%准确率，优于纯数据驱动模型。
- 适合研究手语认知、可解释性模型及无障碍技术的开发者。
为美国手语（ASL）开发的语言模型可显著提升手语使用者的技术可及性。在训练孤立手语识别（ISR）和手语到英语翻译等任务时，现有数据集提供标注的手语视频样本。为增强模型的泛化能力和可解释性，我们引入了美国手语知识图谱（ASLKG），其由12个专家语言学资料来源构建而成。利用ASLKG，我们训练了神经符号模型用于三项手语理解任务：在孤立手语识别任务上达到91%准确率；对未见手语的语义特征预测准确率为14%；对YouTube-ASL视频的主题分类准确率为36%。
原文摘要 · Abstract (English)
Language models for American Sign Language (ASL) could make language technologies substantially more accessible to those who sign. To train models on tasks such as isolated sign recognition (ISR) and ASL-to-English translation, datasets provide annotated video examples of ASL signs. To facilitate the generalizability and explainability of these models, we introduce the American Sign Language Knowledge Graph (ASLKG), compiled from twelve sources of expert linguistic knowledge. We use the ASLKG to train neuro-symbolic models for 3 ASL understanding tasks, achieving accuracies of 91% on ISR, 14% for predicting the semantic features of unseen signs, and 36% for classifying the topic of Youtube-ASL videos.
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