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        <title>DeepBlueAI at PragTag-2023:Ensemble-based Text Classification Approaches under Limited Data Resources</title>
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        <namePart type="given">Zhipeng</namePart>
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            <title>Proceedings of the 10th Workshop on Argument Mining</title>
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            <namePart type="given">Milad</namePart>
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            <namePart type="given">Chung-Chi</namePart>
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    <abstract>Due to the scarcity of review data and the high annotation cost, in this paper, we primarily delve into the fine-tuning of pretrained models using limited data. To enhance the robustness of the model, we employ adversarial training techniques. By introducing subtle perturbations, we compel the model to better cope with adversarial attacks, thereby increasing the stability of the model in input data. We utilize pooling techniques to aid the model in extracting critical information, reducing computational complexity, and improving the model‘s generalization capability. Experimental results demonstrate the effectiveness of our proposed approach on a review paper dataset with limited data volume.</abstract>
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    <identifier type="doi">10.18653/v1/2023.argmining-1.23</identifier>
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%0 Conference Proceedings
%T DeepBlueAI at PragTag-2023:Ensemble-based Text Classification Approaches under Limited Data Resources
%A Luo, Zhipeng
%A Wang, Jiahui
%A Guo, Yihao
%Y Alshomary, Milad
%Y Chen, Chung-Chi
%Y Muresan, Smaranda
%Y Park, Joonsuk
%Y Romberg, Julia
%S Proceedings of the 10th Workshop on Argument Mining
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F luo-etal-2023-deepblueai
%X Due to the scarcity of review data and the high annotation cost, in this paper, we primarily delve into the fine-tuning of pretrained models using limited data. To enhance the robustness of the model, we employ adversarial training techniques. By introducing subtle perturbations, we compel the model to better cope with adversarial attacks, thereby increasing the stability of the model in input data. We utilize pooling techniques to aid the model in extracting critical information, reducing computational complexity, and improving the model‘s generalization capability. Experimental results demonstrate the effectiveness of our proposed approach on a review paper dataset with limited data volume.
%R 10.18653/v1/2023.argmining-1.23
%U https://aclanthology.org/2023.argmining-1.23/
%U https://doi.org/10.18653/v1/2023.argmining-1.23
%P 202-206

Markdown (Informal)

[DeepBlueAI at PragTag-2023:Ensemble-based Text Classification Approaches under Limited Data Resources](https://aclanthology.org/2023.argmining-1.23/) (Luo et al., ArgMining 2023)

• DeepBlueAI at PragTag-2023:Ensemble-based Text Classification Approaches under Limited Data Resources (Luo et al., ArgMining 2023)

ACL

• Zhipeng Luo, Jiahui Wang, and Yihao Guo. 2023. DeepBlueAI at PragTag-2023:Ensemble-based Text Classification Approaches under Limited Data Resources. In Proceedings of the 10th Workshop on Argument Mining, pages 202–206, Singapore. Association for Computational Linguistics.