Improving Zero-Shot Stance Detection by Infusing Knowledge from Large Language Models
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5618-6_11
Reference26 articles.
1. Allaway, E., McKeown, K.: Zero-shot stance detection: a dataset and model using generalized topic representations. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 8913–8931. Association for Computational Linguistics (2020)
2. Liang, B., Chen, Z., Gui, L., He, Y., Yang, M., Xu, R.: Zero-shot stance detection via contrastive learning. In: Proceedings of the ACM Web Conference, pp. 2738–2747. ACM (2022)
3. He, Z., Mokhberian, N., Lerman, K.: Infusing knowledge from wikipedia to enhance stance detection. In: Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pp. 71–77. Association for Computational Linguistics (2022)
4. Lin, Y., et al.: BertGCN: transductive text classification by combining GNN and BERT. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 1456–1462. Association for Computational Linguistics (2021)
5. Kocoń, J., et al.: ChatGPT: Jack of all trades, master of none. Inf. Fusion 99 (2023)
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