research-article

Knowledge-enhanced Prompt-tuning for Stance Detection

Authors:

Cheng PengAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 22, Issue 6

Article No.: 159, Pages 1 - 20

https://doi.org/10.1145/3588767

Published: 16 June 2023 Publication History

Abstract

Investigating public attitudes on social media is important in opinion mining systems. Stance detection aims to analyze the attitude of an opinionated text (e.g., favor, neutral, or against) toward a given target. Existing methods mainly address this problem from the perspective of fine-tuning. Recently, prompt-tuning has achieved success in natural language processing tasks. However, conducting prompt-tuning methods for stance detection in real-world remains a challenge for several reasons: (1) The text form of stance detection is usually short and informal, which makes it difficult to design label words for the verbalizer. (2) The tweet text may not explicitly give the attitude. Instead, users may use various hashtags or background knowledge to express stance-aware perspectives. In this article, we first propose a prompt-tuning-based framework that performs stance detection in a cloze question manner. Specifically, a knowledge-enhanced prompt-tuning framework (KEprompt) method is designed, which consists of an automatic verbalizer (AutoV) and background knowledge injection (BKI). Specifically, in AutoV, we introduce a semantic graph to build a better mapping from the predicted word of the pretrained language model and detection labels. In BKI, we first propose a topic model for learning hashtag representation and introduce ConceptGraph as the supplement of the target. At last, we present a challenging dataset for stance detection, where all stance categories are expressed in an implicit manner. Extensive experiments on a large real-world dataset demonstrate the superiority of KEprompt over state-of-the-art methods.

References

[1]

Emily Allaway and Kathleen Mckeown. 2020. Zero-shot stance detection: A dataset and model using generalized topic representations. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). 8913–8931.

[2]

Emily Allaway and Kathleen R. McKeown. 2020. Zero-shot stance detection: A dataset and model using generalized topic representations. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 8913–8931.

[3]

I. Augenstein, T. Rocktaeschel, A. Vlachos, and K. Bontcheva. 2016. Stance detection with bidirectional conditional encoding. In Proceedings of the Conference on Empirical Methods in Natural Language Processing.

[4]

Erik Cambria, Qian Liu, Sergio Decherchi, Frank Xing, and Kenneth Kwok. 2022. SenticNet 7: A commonsense-based neurosymbolic AI framework for explainable sentiment analysis. In Proceedings of the International Conference on Language Resources and Evaluation.

[5]

Erik Cambria, Soujanya Poria, Devamanyu Hazarika, and Kenneth Kwok. 2018. SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. In Proceeedings of the 32nd AAAI Conference on Artificial Intelligence.

[6]

Alessandra Teresa Cignarella, Cristina Bosco, and Paolo Rosso. 2022. Do dependency relations help in the task of stance detection? In Proceedings of the 3rd Workshop on Insights from Negative Results in NLP. Association for Computational Linguistics, 10–17.

[7]

Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, and Nigel Collier. 2020. 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. 1715–1724.

[8]

Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, and Nigel Collier. 2021. Synthetic examples improve cross-target generalization: A study on stance detection on a Twitter corpus. In Proceedings of the 11th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Association for Computational Linguistics, 181–187.

[9]

Yuhao Dan, Jie Zhou, Qin Chen, Qingchun Bai, and Liang He. 2022. Enhancing class understanding via prompt-tuning for zero-shot text classification. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 4303–4307.

[10]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 4171–4186.

[11]

Kuntal Dey, Ritvik Shrivastava, and Saroj Kaushik. 2018. Topical stance detection for Twitter: A two-phase LSTM model using attention. In Proceedings of the European Conference on Information Retrieval. Springer, 529–536.

[12]

Jiachen Du, Ruifeng Xu, Yulan He, and Lin Gui. 2017. Stance classification with target-specific neural attention networks. In Proceedings of the International Joint Conferences on Artificial Intelligence.

[13]

Tianyu Gao, Adam Fisch, and Danqi Chen. 2021. Making pre-trained language models better few-shot learners. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. Association for Computational Linguistics, 3816–3830.

[14]

Momchil Hardalov, Arnav Arora, Preslav Nakov, and Isabelle Augenstein. 2022. Few-shot cross-lingual stance detection with sentiment-based pre-training. In Proceedings of the AAAI Conference on Artificial Intelligence. 10729–10737.

[15]

Zihao He, Negar Mokhberian, and Kristina Lerman. 2022. Infusing Wikipedia knowledge to enhance stance detection. arXiv preprint arXiv:2204.03839 (2022).

[16]

Shengding Hu, Ning Ding, Huadong Wang, Zhiyuan Liu, Juanzi Li, and Maosong Sun. 2021. Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification. arXiv preprint arXiv:2108.02035 (2021).

[17]

Shengding Hu, Ning Ding, Huadong Wang, Zhiyuan Liu, Jingang Wang, Juanzi Li, Wei Wu, and Maosong Sun. 2022. Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2225–2240.

[18]

Binxuan Huang, Yanglan Ou, and Kathleen M. Carley. 2018. Aspect level sentiment classification with attention-over-attention neural networks. In Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation. Springer, 197–206.

[19]

Rachna Jain, Deepak Kumar Jain, Dharana, and Nitika Sharma. 2022. Fake news classification: A quantitative research description. ACM Trans. Asian Low Resour. Lang. Inf. Process. 21, 1 (2022), 3:1–3:17.

Digital Library

[20]

Yan Jiang, Jinhua Gao, Huawei Shen, and Xueqi Cheng. 2022. Few-shot stance detection via target-aware prompt distillation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 837–847.

Digital Library

[21]

Dilek Küçük and Fazli Can. 2020. Stance detection: A survey. ACM Comput. Surv. 53, 1 (2020), 1–37.

Digital Library

[22]

Chengxi Li, Feiyu Gao, Jiajun Bu, Lu Xu, Xiang Chen, Yu Gu, Zirui Shao, Qi Zheng, Ningyu Zhang, Yongpan Wang et al. 2021. Sentiprompt: Sentiment knowledge enhanced prompt-tuning for aspect-based sentiment analysis. arXiv preprint arXiv:2109.08306 (2021).

[23]

Chen Li, Hao Peng, Jianxin Li, Lichao Sun, Lingjuan Lyu, Lihong Wang, Philip S. Yu, and Lifang He. 2022. Joint stance and rumor detection in hierarchical heterogeneous graph. IEEE Trans. Neural Netw. Learn. Syst. 33, 6 (2022), 2530–2542.

[24]

Yingjie Li and Cornelia Caragea. 2019. Multi-task stance detection with sentiment and stance lexicons. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 6299–6305.

[25]

Yingjie Li, Tiberiu Sosea, Aditya Sawant, Ajith Jayaraman Nair, Diana Inkpen, and Cornelia Caragea. 2021. P-stance: A large dataset for stance detection in political domain. In Findings of the Association for Computational Linguistics.

[26]

Bin Liang, Zixiao Chen, Lin Gui, Yulan He, Min Yang, and Ruifeng Xu. 2022. Zero-shot stance detection via contrastive learning. In Proceedings of the ACM Web Conference. 2738–2747.

Digital Library

[27]

Bin Liang, Yonghao Fu, Lin Gui, Min Yang, Jiachen Du, Yulan He, and Ruifeng Xu. 2021. Target-adaptive graph for cross-target stance detection. In Proceedings of the Web Conference. 3453–3464.

Digital Library

[28]

Rui Liu, Zheng Lin, Yutong Tan, and Weiping Wang. 2021. Enhancing zero-shot and few-shot stance detection with commonsense knowledge graph. In Findings of the Association for Computational Linguistics. 3152–3157.

[29]

Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiao-Dan Zhu, and Colin Cherry. 2016. SemEval-2016 task 6: Detecting stance in tweets. In Proceedings of the 10th International Workshop on Semantic Evaluation. 31–41.

[30]

Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, and Gerhard Weikum. 2019. STANCY: Stance classification based on consistency cues. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Association for Computational Linguistics, 6413–6418. DOI:

[31]

Sujata Rani and Parteek Kumar. 2022. Aspect-based sentiment analysis using dependency parsing. ACM Trans. Asian Low Resour. Lang. Inf. Process. 21, 3 (2022), 56:1–56:19.

Digital Library

[32]

Timo Schick, Helmut Schmid, and Hinrich Schütze. 2020. Automatically identifying words that can serve as labels for few-shot text classification. In Proceedings of the 28th International Conference on Computational Linguistics. 5569–5578.

[33]

Timo Schick and Hinrich Schütze. 2021. Exploiting cloze-questions for few-shot text classification and natural language inference. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 255–269.

[34]

Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020. AutoPrompt: Eliciting knowledge from language models with automatically generated prompts. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 4222–4235.

[35]

Parinaz Sobhani, Diana Inkpen, and Xiaodan Zhu. 2017. A dataset for multi-target stance detection. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. 551–557.

[36]

Swapna Somasundaran and Janyce Wiebe. 2009. Recognizing stances in online debates. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. Association for Computational Linguistics, 226–234.

Digital Library

[37]

Qingying Sun, Zhongqing Wang, Qiaoming Zhu, and Guodong Zhou. 2018. Stance detection with hierarchical attention network. In Proceedings of the 27th International Conference on Computational Linguistics. 2399–2409.

[38]

Duyu Tang, Bing Qin, and Ting Liu. 2016. Aspect level sentiment classification with deep memory network. In Proceedings of the Conference on Empirical Methods in Natural Language Processing.

[39]

Marilyn A. Walker, Pranav Anand, Robert Abbott, and Ricky Grant. 2012. Stance classification using dialogic properties of persuasion. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 592–596.

[40]

Limin Wang and Dexin Wang. 2021. Solving stance detection on tweets as multi-domain and multi-task text classification. IEEE Access 9 (2021), 157780–157789.

[41]

Penghui Wei, Junjie Lin, and Wenji Mao. 2018. Multi-target stance detection via a dynamic memory-augmented network. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 1229–1232.

Digital Library

[42]

Penghui Wei and Wenji Mao. 2019. Modeling transferable topics for cross-target stance detection. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 1173–1176.

Digital Library

[43]

Chang Xu, Cecile Paris, Surya Nepal, and Ross Sparks. 2018. Cross-target stance classification with self-attention networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 778–783.

[44]

Min Yang, Qingnan Jiang, Ying Shen, Qingyao Wu, Zhou Zhao, and Wei Zhou. 2019. Hierarchical human-like strategy for aspect-level sentiment classification with sentiment linguistic knowledge and reinforcement learning. Neural Netw. 117 (2019), 240–248.

Digital Library

[45]

Min Yang, Wei Zhao, Lei Chen, Qiang Qu, Zhou Zhao, and Ying Shen. 2019. Investigating the transferring capability of capsule networks for text classification. Neural Netw. 118 (2019), 247–261.

Digital Library

[46]

Bowen Zhang, Min Yang, Xutao Li, Yunming Ye, Xiaofei Xu, and Kuai Dai. 2020. Enhancing cross-target stance detection with transferable semantic-emotion knowledge. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 3188–3197.

[47]

Chen Zhang, Qiuchi Li, and Dawei Song. 2019. Aspect-based sentiment classification with aspect-specific graph convolutional networks. arXiv preprint arXiv:1909.03477 (2019).

[48]

Xin Zhang, Jianhua Yuan, Yanyan Zhao, and Bing Qin. 2021. Knowledge enhanced target-aware stance detection on tweets. In Proceedings of the China Conference on Knowledge Graph and Semantic Computing. Springer, 171–184.

[49]

Yazhou Zhang, Prayag Tiwari, Dawei Song, Xiaoliu Mao, Panpan Wang, Xiang Li, and Hari Mohan Pandey. 2021. Learning interaction dynamics with an interactive LSTM for conversational sentiment analysis. Neural Netw. 133 (2021), 40–56.

Cited By

Keber MGrubišić IBarešić AJović A(2024)A Review on Neuro-symbolic AI Improvements to Natural Language Processing2024 47th MIPRO ICT and Electronics Convention (MIPRO)10.1109/MIPRO60963.2024.10569741(66-72)Online publication date: 20-May-2024
https://doi.org/10.1109/MIPRO60963.2024.10569741
Ding DChen RJing LZhang BHuang XDong LZhao XSong G(2024)Cross-Target Stance Detection by Exploiting Target Analytical PerspectivesICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448397(10651-10655)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448397

Index Terms

Knowledge-enhanced Prompt-tuning for Stance Detection
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks

Recommendations

Stance Detection: A Survey

Automatic elicitation of semantic information from natural language texts is an important research problem with many practical application areas. Especially after the recent proliferation of online content through channels such as social media sites, ...
Twitter stance detection using deep learning model with FastText Embedding
CACML '23: Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning

The interactivity of social media platforms allows a large number of users to comment on different political or social issues to express their views, and identifying users' stances from online comment texts helps the government to monitor public opinion ...
Stance Detection with Knowledge Enhanced BERT
Artificial Intelligence
Abstract
Microblog stance detection aims to determine an author’s stance (for or against) towards a specific topic or claim in a post. It has become a key component in applications like truth finding, intention mining and rumor detection. Recently, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 22, Issue 6

June 2023

635 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3604597

Editor:
Imed Zitouni
Google, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 June 2023

Online AM: 23 March 2023

Accepted: 13 March 2023

Revised: 08 January 2023

Received: 08 October 2022

Published in TALLIP Volume 22, Issue 6

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Stable Support Project for Shenzhen Higher Education Institutions
Research Promotion Project of Key Construction Discipline in Guangdong Province

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
1,021
Total Downloads

Downloads (Last 12 months)578
Downloads (Last 6 weeks)68

Reflects downloads up to 23 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Keber MGrubišić IBarešić AJović A(2024)A Review on Neuro-symbolic AI Improvements to Natural Language Processing2024 47th MIPRO ICT and Electronics Convention (MIPRO)10.1109/MIPRO60963.2024.10569741(66-72)Online publication date: 20-May-2024
https://doi.org/10.1109/MIPRO60963.2024.10569741
Ding DChen RJing LZhang BHuang XDong LZhao XSong G(2024)Cross-Target Stance Detection by Exploiting Target Analytical PerspectivesICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448397(10651-10655)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448397

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents