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https://nccur.lib.nccu.edu.tw/handle/140.119/142662
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Title: | 使用子主題建模的零樣本立場偵測 Zero-shot stance detection with subtopic modeling |
Authors: | 穆永綸 Mu, Yung-Lun |
Contributors: | 黃瀚萱 Huang, Hen-Hsen 穆永綸 Mu, Yung-Lun |
Keywords: | 立場偵測 遷移學習 零樣本學習 Stance detection Transfer learning Zero-shot learning |
Date: | 2022 |
Issue Date: | 2022-12-02 15:23:22 (UTC+8) |
Abstract: | 立場偵測任務有助我們初步了解發文者對特定主題的態度,但立場偵測模型的建立往往受限於標籤資料的不足,本研究針對可充分運用既有標籤資料的零樣本立場偵測任務進行研究,此外,由於網路社群媒體儼然是當前了解民眾意向的重要媒介,我們聚焦網路社群文本的立場偵測任務。
我們的模型同時考量了「主題獨立」以及「主題相依」2 種可遷移學習的文本特徵,提升模型的泛化能力,利用資料集的立場標籤與主題資訊,進行對抗學習與監督對比學習:透過立場與主題分類的對抗學習,加強模型對「主題獨立」的特徵表示,另以 2 種監督對比學習設定,在強化模型對「主題獨立」特徵表示的同時,也能凸顯「主題相依」的特徵表示,實驗顯示此方法有助於立場偵測任務。另我們也嘗試透過LDA 子主題建模,以LDA 主題模型產生的子主題詞組增加輸入模型的語意資訊,然由於 LDA 主題模型是以非監督學習方法建模,其過程並未考量立場偵測任務需求,使得子主題詞組的方法未能在每個主題都發揮效果。 Stance detection(SD) tasks give us an initial look at the attitudes of author on a particular topic. Though fully supervised SD model can achieve favorable performance, the lack of labeled data limits its availability. In this work, we focus on zero-shot stance detection(ZSSD). Besides, as online social media get popular, we take textual content from online community as our research target. To generalize stance features for unseen target, we consider both ” topic-invariant” and ” topic-dependent” features that are transferable between source and target domain. Specifically, to make the use of ”topic-invariant” features, a stance classifier and a topic discriminator is set for adversarial learning. To further generalize ” topic-invariant” and ” topic-dependent” stance features, contrastive learning strategy is deployed using the stance label and topic information from data-set. Experiments on benchmark data-set show that the proposed approach is feasible. Furthermore, we build LDA subtopic model for each target and augment the semantic information through the subtopic words. However, since our subtopic modeling task is unsupervised and independent from stance detection task, the beneficial of subtopic modeling turned out to be unstable. |
Reference: | [1] Abeer ALDayel and Walid Magdy. Stance detection on social media: State of the art and trends. Information Processing Management, 58(4):102597, 2021. [2] Emily Allaway and Kathleen McKeown. 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), pages 8913–8931, Online, November 2020. Association for Computational Linguistics. [3] Emily Allaway, Malavika Srikanth, and Kathleen McKeown. 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, pages 4756–4767, Online, June 2021. Association for Computational Linguistics. [4] Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, and Kalina Bontcheva. Stance detection with bidirectional conditional encoding. 2016. [5] David M Blei, Andrew Y Ng, and Michael I Jordan. Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993–1022, 2003. [6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. [7] Rui Dong, Yizhou Sun, Lu Wang, Yupeng Gu, and Yuan Zhong. Weakly-guided user stance prediction via joint modeling of content and social interaction. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, nov 2017. [8] Matthew Hoffman, Francis Bach, and David Blei. Online learning for latent dirichlet allocation. In J. Lafferty, C. Williams, J. Shawe-Taylor, R. Zemel, and A. Culotta, editors, Advances in Neural Information Processing Systems, volume 23. Curran Associates, Inc., 2010. [9] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. 04 2020. [10] Dilek Küçük and Fazli Can. Stance detection. ACM Computing Surveys, 53(1):1–37, jan 2021. [11] Stephen Wai Hang Kwok, Sai Kumar Vadde, and Guanjin” Wang. Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: Machine learning analysis. J Med Internet Res, 23(5):e26953, May 2021. [12] Bin Liang, Zixiao Chen, Lin Gui, Yulan He, Min Yang, and Ruifeng Xu. Zero-shot stance detection via contrastive learning. In Proceedings of the ACM Web Conference 2022. ACM, apr 2022. [13] Bin Liang, Yonghao Fu, Lin Gui, Min Yang, Jiachen Du, Yulan He, and Ruifeng Xu. Target-adaptive graph for cross-target stance detection. In Proceedings of the Web Conference 2021. ACM, apr 2021. [14] Bin Liang, Qinglin Zhu, Xiang Li, Min Yang, Lin Gui, Yulan He, and Ruifeng Xu. JointCL: A joint contrastive learning framework for zero-shot stance detection. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 81–91, Dublin, Ireland, May 2022. Association for Computational Linguistics. [15] Rui Liu, Zheng Lin, Peng Fu, Yuanxin Liu, and Weiping Wang. Connecting targets via latent topics and contrastive learning: A unified framework for robust zero-shot and few- shot stance detection. In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 7812–7816, 2022. [16] Sandra Maria Correia Loureiro, João Guerreiro, and Iis Tussyadiah. Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research, 129:911–926, 2021. [17] Sara Mifrah. Topic modeling coherence: A comparative study between LDA and NMF models using COVID’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering, 9(4):5756–5761, aug 2020. [18] Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. SemEval-2016 task 6: Detecting stance in tweets. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 31–41, San Diego, California, June 2016. Association for Computational Linguistics. [19] Chang Xu, Cecile Paris, Surya Nepal, and Ross Sparks. Cross-target stance classification with self-attention networks. 05 2018. [20] Guido Zarrella and Amy Marsh. MITRE at SemEval-2016 task 6: Transfer learning for stance detection. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 458–463, San Diego, California, June 2016. Association for Computational Linguistics. [21] Yuan Zhang, Regina Barzilay, and Tommi Jaakkola. Aspect-augmented adversarial networks for domain adaptation. Transactions of the Association for Computational Linguistics, 5:515–528, dec 2017. |
Description: | 碩士 國立政治大學 資訊科學系碩士在職專班 109971003 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109971003 |
Data Type: | thesis |
DOI: | 10.6814/NCCU202201678 |
Appears in Collections: | [資訊科學系碩士在職專班] 學位論文
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