@inproceedings{duan-etal-2023-cda,
title = "{CDA}: A Contrastive Data Augmentation Method for {A}lzheimer{'}s Disease Detection",
author = "Duan, Junwen and
Wei, Fangyuan and
Liu, Jin and
Li, Hongdong and
Liu, Tianming and
Wang, Jianxin",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.114",
doi = "10.18653/v1/2023.findings-acl.114",
pages = "1819--1826",
abstract = "Alzheimer{'}s Disease (AD) is a neurodegenerative disorder that significantly impacts a patient{'}s ability to communicate and organize language. Traditional methods for detecting AD, such as physical screening or neurological testing, can be challenging and time-consuming. Recent research has explored the use of deep learning techniques to distinguish AD patients from non-AD patients by analysing the spontaneous speech. These models, however, are limited by the availability of data. To address this, we propose a novel contrastive data augmentation method, which simulates the cognitive impairment of a patient by randomly deleting a proportion of text from the transcript to create negative samples. The corrupted samples are expected to be in worse conditions than the original by a margin. Experimental results on the benchmark ADReSS Challenge dataset demonstrate that our model achieves the best performance among language-based models.",
}
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<abstract>Alzheimer’s Disease (AD) is a neurodegenerative disorder that significantly impacts a patient’s ability to communicate and organize language. Traditional methods for detecting AD, such as physical screening or neurological testing, can be challenging and time-consuming. Recent research has explored the use of deep learning techniques to distinguish AD patients from non-AD patients by analysing the spontaneous speech. These models, however, are limited by the availability of data. To address this, we propose a novel contrastive data augmentation method, which simulates the cognitive impairment of a patient by randomly deleting a proportion of text from the transcript to create negative samples. The corrupted samples are expected to be in worse conditions than the original by a margin. Experimental results on the benchmark ADReSS Challenge dataset demonstrate that our model achieves the best performance among language-based models.</abstract>
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%0 Conference Proceedings
%T CDA: A Contrastive Data Augmentation Method for Alzheimer’s Disease Detection
%A Duan, Junwen
%A Wei, Fangyuan
%A Liu, Jin
%A Li, Hongdong
%A Liu, Tianming
%A Wang, Jianxin
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F duan-etal-2023-cda
%X Alzheimer’s Disease (AD) is a neurodegenerative disorder that significantly impacts a patient’s ability to communicate and organize language. Traditional methods for detecting AD, such as physical screening or neurological testing, can be challenging and time-consuming. Recent research has explored the use of deep learning techniques to distinguish AD patients from non-AD patients by analysing the spontaneous speech. These models, however, are limited by the availability of data. To address this, we propose a novel contrastive data augmentation method, which simulates the cognitive impairment of a patient by randomly deleting a proportion of text from the transcript to create negative samples. The corrupted samples are expected to be in worse conditions than the original by a margin. Experimental results on the benchmark ADReSS Challenge dataset demonstrate that our model achieves the best performance among language-based models.
%R 10.18653/v1/2023.findings-acl.114
%U https://aclanthology.org/2023.findings-acl.114
%U https://doi.org/10.18653/v1/2023.findings-acl.114
%P 1819-1826
Markdown (Informal)
[CDA: A Contrastive Data Augmentation Method for Alzheimer’s Disease Detection](https://aclanthology.org/2023.findings-acl.114) (Duan et al., Findings 2023)
ACL