对BERT还是不对BERT：阿尔茨海默病检测的基于语音和记忆的方法比较
To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection
摘要：
鉴于AD的高患病率和传统方法的高成本，与自动检测阿尔茨海默病（AD）相关的研究非常重要。由于AD显著影响自发语音的内容和声学，因此自然语言处理和机器学习为可靠地检测AD提供了有前景的技术。我们在最近的ADReSS挑战数据集上比较和对比了两种用于AD检测的方法的性能：1）使用基于领域知识的手工制作的特征来捕获语言和声学现象，以及2）微调基于Transformer（BERT）的序列分类模型的双向编码器表示。我们还比较了多个基于特征的回归模型的神经心理评分任务的挑战。我们观察到，微调BERT模型，考虑到语言学在认知障碍检测中的相对重要性，在AD检测任务上优于基于特征的方法。
英文摘要：
Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly affects the content and acoustics of spontaneous speech, natural language processing and machine learning provide promising techniques for reliably detecting AD. We compare and contrast the performance of two such approaches for AD detection on the recent ADReSS challenge dataset: 1) using domain knowledge-based hand-crafted features that capture linguistic and acoustic phenomena, and 2) fine-tuning Bidirectional Encoder Representations from Transformer (BERT)-based sequence classification models. We also compare multiple feature-based regression models for a neuropsychological score task in the challenge. We observe that fine-tuned BERT models, given the relative importance of linguistics in cognitive impairment detection, outperform feature-based approaches on the AD detection task.
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