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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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 (2020)
Allaway, E., Srikanth, M., Mckeown, K.: Adversarial learning for zero-shot stance detection on social media. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 4756–4767 (2021)
Augenstein, I., Rocktäschel, T., Vlachos, A., Bontcheva, K.: Stance detection with bidirectional conditional encoding. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. pp. 876–885 (2016)
Conforti, C., Berndt, J., Pilehvar, M.T., Giannitsarou, C., Toxvaerd, F., Collier, N.: Will-they-won’t-they: a very large dataset for stance detection on twitter. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 1715–1724 (2020)
Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030–2096 (2016)
Ghosal, D., Hazarika, D., Roy, A., Majumder, N., Mihalcea, R., Poria, S.: Kingdom: knowledge-guided domain adaptation for sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 3198–3210 (2020)
Glandt, K., Khanal, S., Li, Y., Caragea, D., Caragea, C.: Stance detection in COVID-19 tweets. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing vol. 1: Long Papers. pp. 1596–1611 (2021)
Goodfellow, I.J., et al.: Generative adversarial networks (2014). arXiv preprint arXiv:1406.2661 (2014)
Hardalov, M., Arora, A., Nakov, P., Augenstein, I.: Few-shot cross-lingual stance detection with sentiment-based pre-training. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 10729–10737 (2022)
Kenton, J.D.M.W.C., Toutanova, L.K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT. pp. 4171–4186 (2019)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Küçük, D., Can, F.: Stance detection: a survey. ACM Comput. Surv. (CSUR) 53(1), 1–37 (2020)
Kumar, S., Carley, K.M.: Tree LSTMs with convolution units to predict stance and rumor veracity in social media conversations. In: Proceedings of the 57th Annual meeting of the Association for Computational Linguistics. pp. 5047–5058 (2019)
Lai, M., Cignarella, A.T., Farías, D.I.H., Bosco, C., Patti, V., Rosso, P.: Multilingual stance detection in social media political debates. Comput. Speech Lang. 63, 101075 (2020)
Li, Y., Caragea, C.: Multi-task stance detection with sentiment and stance lexicons. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). pp. 6299–6305 (2019)
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 2022. pp. 2738–2747 (2022)
Liang, B., et al.: Target-adaptive graph for cross-target stance detection. In: Proceedings of the Web Conference 2021. pp. 3453–3464 (2021)
Liu, R., Lin, Z., Tan, Y., Wang, W.: Enhancing zero-shot and few-shot stance detection with commonsense knowledge graph. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. pp. 3152–3157 (2021)
Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., Cherry, C.: Semeval-2016 task 6: detecting stance in tweets. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016). pp. 31–41 (2016)
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling Relational Data with Graph Convolutional Networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7167–7176 (2017)
Wang, Z., Wang, Q., Lv, C., Cao, X., Fu, G.: Unseen target stance detection with adversarial domain generalization. In: 2020 International Joint Conference on Neural Networks (IJCNN). pp. 1–8. IEEE (2020)
Wei, P., Mao, W.: Modeling transferable topics for cross-target stance detection. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 1173–1176 (2019)
Xie, F., Zhang, Z., Zhao, X., Zou, J., Zhou, B., Tan, Y.: Adversarial learning-based stance classifier for COVID-19-related health policies. arXiv preprint arXiv:2209.04631 (2022)
Xu, C., Paris, C., Nepal, S., Sparks, R.: Cross-target stance classification with self-attention networks. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). pp. 778–783 (2018)
Zhou, J., Tian, J., Wang, R., Wu, Y., Xiao, W., He, L.: Sentix: a sentiment-aware pre-trained model for cross-domain sentiment analysis. In: Proceedings of the 28th International Conference on Computational Linguistics. pp. 568–579 (2020)
Zhu, Q., Liang, B., Sun, J., Du, J., Zhou, L., Xu, R.: Enhancing zero-shot stance detection via targeted background knowledge. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 2070–2075 (2022)
Acknowledgements
This work is supported by a grant from the Social and Science Foundation of Liaoning Province (No. L20BTQ008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-99-6207-5_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-6206-8
Online ISBN: 978-981-99-6207-5
eBook Packages: Computer ScienceComputer Science (R0)