论文标题
伯特或不伯特：比较阿尔茨海默氏病检测的言语和基于语言的方法
To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection
论文作者
论文摘要
鉴于AD的高流行和传统方法的高成本，与自动检测阿尔茨海默氏病（AD）有关的研究很重要。由于广告会显着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提供了有希望的技术。我们比较和对比两种此类AD检测方法的性能在最近的Adress挑战数据集上的性能：1）使用基于域知识的手工制作的功能，捕获语言和声学现象，以及2）基于Transformer（BERT）的基于基于Transficeer的序列分类模型的微调双向编码器。我们还比较了挑战中神经心理评分任务的多个基于功能的回归模型。我们观察到，鉴于语言学在认知障碍检测中的相对重要性，对AD检测任务的基于特征的方法的相对重要性。
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.
