Abstract
Zero-shot stance detection intends to detect previously unseen targets’ stances in the testing phase. However, achieving this goal can be difficult, as it requires minimizing the domain transfer 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 transferable knowledge from the source targets, thereby enabling the model to generalize well to unseen targets. Additionally, we incorporate sentiment information and common sense knowledge into the contextual representation to further enhance the model’s understanding. Experimental results on several datasets reveal that our method achieves excellent performance, demonstrating its validity and feasibility.
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This work is supported by a grant from the Social and Science Foundation of Liaoning Province (No. L20BTQ008)
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Wang, C., Zhang, Y., Yu, X., Liu, G., Chen, F., Lin, H. (2023). Adversarial Network with External Knowledge for Zero-Shot Stance Detection. In: Sun, M., et al. Chinese Computational Linguistics. CCL 2023. Lecture Notes in Computer Science(), vol 14232. Springer, Singapore. https://doi.org/10.1007/978-981-99-6207-5_26
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DOI: https://doi.org/10.1007/978-981-99-6207-5_26
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