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    <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.</abstract>
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%0 Conference Proceedings
%T Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection
%A Guo, Zhiqiang
%A Liu, Zhaoci
%A Ling, Zhenhua
%A Wang, Shijin
%A Jin, Lingjing
%A Li, Yunxia
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F guo-etal-2020-text
%X 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.
%R 10.18653/v1/2020.coling-main.542
%U https://aclanthology.org/2020.coling-main.542
%U https://doi.org/10.18653/v1/2020.coling-main.542
%P 6161-6171

Markdown (Informal)

[Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection](https://aclanthology.org/2020.coling-main.542) (Guo et al., COLING 2020)

• Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection (Guo et al., COLING 2020)

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.