Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection
Zhiqiang Guo, Zhaoci Liu, Zhenhua Ling, Shijin Wang, Lingjing Jin, Yunxia Li
Abstract
Data scarcity is always a constraint on analyzing speech transcriptions for automatic Alzheimer’s disease (AD) detection, especially when the subjects are non-English speakers. To deal with this issue, this paper first proposes a contrastive learning method to obtain effective representations for text classification based on monolingual embeddings of BERT. Furthermore, a cross-lingual data augmentation method is designed by building autoencoders to learn the text representations shared by both languages. Experiments on a Mandarin AD corpus show that the contrastive learning method can achieve better detection accuracy than conventional CNN-based and BERTbased methods. Our cross-lingual data augmentation method also outperforms other compared methods when using another English AD corpus for augmentation. Finally, a best detection accuracy of 81.6% is obtained by our proposed methods on the Mandarin AD corpus.
BibTeX
@inproceedings{guo-etal-2020-text,
title = "Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for {A}lzheimer{'}s Disease Detection",
author = "Guo, Zhiqiang and
Liu, Zhaoci and
Ling, Zhenhua and
Wang, Shijin and
Jin, Lingjing and
Li, Yunxia",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.542/",
doi = "10.18653/v1/2020.coling-main.542",
pages = "6161--6171"
}