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        <title>Adversarial Network with External Knowledge for Zero-Shot Stance Detection</title>
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            <title>Proceedings of the 22nd Chinese National Conference on Computational Linguistics</title>
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            <namePart type="given">Maosong</namePart>
            <namePart type="family">Sun</namePart>
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    <abstract>“Zero-shot stance detection intends to detect previously unseen targets’ stances in the testingphase. However, achieving this goal can be difficult, as it requires minimizing the domain trans-fer between different targets, and improving the model’s inference and generalization abilities. To address this challenge, we propose an adversarial network with external knowledge (ANEK)model. Specifically, we adopt adversarial learning based on pre-trained models to learn transfer-able knowledge from the source targets, thereby enabling the model to generalize well to unseentargets. Additionally, we incorporate sentiment information and common sense knowledge intothe contextual representation to further enhance the model’s understanding. Experimental re-sults on several datasets reveal that our method achieves excellent performance, demonstratingits validity and feasibility.”</abstract>
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%0 Conference Proceedings
%T Adversarial Network with External Knowledge for Zero-Shot Stance Detection
%A Chunling, Wang
%A Yijia, Zhang
%A Xingyu, Yu
%A Guantong, Liu
%A Fei, Chen
%A Hongfei, Lin
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G English
%F chunling-etal-2023-adversarial
%X “Zero-shot stance detection intends to detect previously unseen targets’ stances in the testingphase. However, achieving this goal can be difficult, as it requires minimizing the domain trans-fer between different targets, and improving the model’s inference and generalization abilities. To address this challenge, we propose an adversarial network with external knowledge (ANEK)model. Specifically, we adopt adversarial learning based on pre-trained models to learn transfer-able knowledge from the source targets, thereby enabling the model to generalize well to unseentargets. Additionally, we incorporate sentiment information and common sense knowledge intothe contextual representation to further enhance the model’s understanding. Experimental re-sults on several datasets reveal that our method achieves excellent performance, demonstratingits validity and feasibility.”
%U https://aclanthology.org/2023.ccl-1.70
%P 824-835

Markdown (Informal)

[Adversarial Network with External Knowledge for Zero-Shot Stance Detection](https://aclanthology.org/2023.ccl-1.70) (Chunling et al., CCL 2023)

• Adversarial Network with External Knowledge for Zero-Shot Stance Detection (Chunling et al., CCL 2023)

ACL

• Wang Chunling, Zhang Yijia, Yu Xingyu, Liu Guantong, Chen Fei, and Lin Hongfei. 2023. Adversarial Network with External Knowledge for Zero-Shot Stance Detection. In Proceedings of the 22nd Chinese National Conference on Computational Linguistics, pages 824–835, Harbin, China. Chinese Information Processing Society of China.