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.
Anthology ID:
2020.coling-main.542
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6161–6171
Language:
URL:
https://aclanthology.org/2020.coling-main.542
DOI:
10.18653/v1/2020.coling-main.542
Bibkey:
Cite (ACL):
Zhiqiang Guo, Zhaoci Liu, Zhenhua Ling, Shijin Wang, Lingjing Jin, and Yunxia Li. 2020. Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6161–6171, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection (Guo et al., COLING 2020)
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