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        <title>SPACL: Shared-Private Architecture based on Contrastive Learning for Multi-domain Text Classification</title>
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        <namePart type="given">Xiong</namePart>
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    <name type="personal">
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    <name type="personal">
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        <namePart type="given">Ouyang</namePart>
        <namePart type="family">Zhouhao</namePart>
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    <abstract>“With the development of deep learning in recent years, text classification research has achieved remarkable results. However, text classification task often requires a large amount of annotated data, and data in different fields often force the model to learn different knowledge. It is often difficult for models to distinguish data labeled in different domains. Sometimes data from different domains can even damage the classification ability of the model and reduce the overall performance of the model. To address these issues, we propose a shared-private architecture based on contrastive learning for multi-domain text classification which can improve both the accuracy and robustness of classifiers. Extensive experiments are conducted on two public datasets. The results of experiments show that the our approach achieves the state-of-the-art performance in multi-domain text classification.”</abstract>
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        <date>2022-10</date>
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%0 Conference Proceedings
%T SPACL: Shared-Private Architecture based on Contrastive Learning for Multi-domain Text Classification
%A Guoding, Xiong
%A Yongmei, Zhou
%A Deheng, Wang
%A Zhouhao, Ouyang
%S Proceedings of the 21st Chinese National Conference on Computational Linguistics
%D 2022
%8 October
%I Chinese Information Processing Society of China
%C Nanchang, China
%G English
%F guoding-etal-2022-spacl
%X “With the development of deep learning in recent years, text classification research has achieved remarkable results. However, text classification task often requires a large amount of annotated data, and data in different fields often force the model to learn different knowledge. It is often difficult for models to distinguish data labeled in different domains. Sometimes data from different domains can even damage the classification ability of the model and reduce the overall performance of the model. To address these issues, we propose a shared-private architecture based on contrastive learning for multi-domain text classification which can improve both the accuracy and robustness of classifiers. Extensive experiments are conducted on two public datasets. The results of experiments show that the our approach achieves the state-of-the-art performance in multi-domain text classification.”
%U https://aclanthology.org/2022.ccl-1.84
%P 958-965

Markdown (Informal)

[SPACL: Shared-Private Architecture based on Contrastive Learning for Multi-domain Text Classification](https://aclanthology.org/2022.ccl-1.84) (Guoding et al., CCL 2022)

• SPACL: Shared-Private Architecture based on Contrastive Learning for Multi-domain Text Classification (Guoding et al., CCL 2022)

ACL

• Xiong Guoding, Zhou Yongmei, Wang Deheng, and Ouyang Zhouhao. 2022. SPACL: Shared-Private Architecture based on Contrastive Learning for Multi-domain Text Classification. In Proceedings of the 21st Chinese National Conference on Computational Linguistics, pages 958–965, Nanchang, China. Chinese Information Processing Society of China.