MultiConAD：用于早期阿尔茨海默病检测的统一多语言对话数据集
MultiConAD: A Unified Multilingual Conversational Dataset for Early Alzheimer's Detection
AI总结：
本研究构建多语言对话数据集MultiConAD，统一16个痴呆相关数据集，涵盖四语言及音频文本，实现包括MCI的细粒度分类，并探索单语与多语训练效果，为早期AD检测提供新资源与见解。
摘要：
痴呆是一种进行性认知综合征，其中阿尔茨海默病（AD）是主要原因。基于对话的AD检测为临床方法提供了一种成本效益高的替代方案，因为语言功能障碍是AD的早期生物标志物。然而，大多数先前研究将AD检测视为二元分类问题，限制了识别轻度认知障碍（MCI）的能力——而MCI是早期干预的关键阶段。此外，研究主要依赖单语言数据集，主要是英语，限制了跨语言的泛化性。为解决这一空白，我们做出了三项关键贡献。首先，我们通过统一16个公开可用的痴呆相关对话数据集，引入了一个新颖的多语言AD检测数据集。该语料库涵盖英语、西班牙语、中文和希腊语，并整合了源自各种认知评估任务的音频和文本数据。其次，我们进行了更细粒度的分类，包括MCI，并使用稀疏和密集文本表示评估了各种分类器。第三，我们在单语言和多语言设置中进行了实验，发现某些语言受益于多语言训练，而其他语言独立表现更好。本研究强调了多语言AD检测中的挑战，并为未来关于特定语言方法和旨在提高模型泛化性和稳健性的技术研究提供了支持。
英文摘要：
Dementia is a progressive cognitive syndrome with Alzheimer's disease (AD) as the leading cause. Conversation-based AD detection offers a cost-effective alternative to clinical methods, as language dysfunction is an early biomarker of AD. However, most prior research has framed AD detection as a binary classification problem, limiting the ability to identify Mild Cognitive Impairment (MCI)-a crucial stage for early intervention. Also, studies primarily rely on single-language datasets, mainly in English, restricting cross-language generalizability. To address this gap, we make three key contributions. First, we introduce a novel, multilingual dataset for AD detection by unifying 16 publicly available dementia-related conversational datasets. This corpus spans English, Spanish, Chinese, and Greek and incorporates both audio and text data derived from a variety of cognitive assessment tasks. Second, we perform finer-grained classification, including MCI, and evaluate various classifiers using sparse and dense text representations. Third, we conduct experiments in monolingual and multilingual settings, finding that some languages benefit from multilingual training while others perform better independently. This study highlights the challenges in multilingual AD detection and enables future research on both language-specific approaches and techniques aimed at improving model generalization and robustness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
