Contrastive learning for stance detection based on theme

Wangyang Yu; Shao Huang

doi:10.1117/12.2684993

19 October 2023 Contrastive learning for stance detection based on theme

Wangyang Yu, Shao Huang

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127092Q (2023) https://doi.org/10.1117/12.2684993
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Stance detection estimates the stance of a user towards a specific target through the text that the user posts. As an important step in public-opinion mining, stance detection has great potential for wide application and provides necessary data for decision-making by governments and enterprises. On social media, people exchange ideas by replies, in which the texts of replies are closely related to the context. Existing works in this regard focus mainly on the method of aggregating the context, but overlook the fact that the theme of texts alters with the progress of the response. Therefore, we propose a stance detection model that can effectively learn the thematic information, where contrastive learning is used to enhance the learning effect of thematic information. Experiments were performed on CreateDebate and Kialo datasets, and our proposed model achieved an accuracy of 68.0% and 69.6% on the two datasets, respectively. Experiments have proved that our method can improve the performance of stance detection.

Citation Download Citation

Wangyang Yu and Shao Huang "Contrastive learning for stance detection based on theme", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127092Q (19 October 2023); https://doi.org/10.1117/12.2684993

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