research-article

Stance Detection with a Multi-Target Adversarial Attention Network

Authors:

Zhongqing Wang,

Huiyan XuAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 22, Issue 2

Article No.: 40, Pages 1 - 21

https://doi.org/10.1145/3544490

Published: 27 December 2022 Publication History

Abstract

Stance detection aims to assign a stance label (in favor or against) to a post towards a specific target. In the literature, there are many studies focusing on this topic, and most of them treat stance detection as a supervised learning task. Therefore, a new classifier needs to be built from scratch on a well-prepared set of ground-truth data whenever predictions are needed for an unseen target. However, it is difficult to annotate the stance of a post, since a stance is a subjective attitude towards a target. Hence, it is necessary to learn the information from unlabeled data or other target data to help stance detection with a certain target. In this study, we propose a multi-target stance detection framework to integrate multi-target data together for stance detection. Since topic and sentiment are two important factors to identify the stance of a post in multi-target data, we propose an adversarial attention network to integrate multi-target data by detecting and connecting topic and sentiment information. In particular, the adversarial network is utilized to determine the topic and the sentiment of each post to collect some target-invariant information for stance detection. In addition, the attention mechanism is utilized to connect posts with a similar topic or sentiment to acquire some key information for stance detection. The experimental results not only demonstrate the effectiveness of the proposed model, but also indicate the importance of the topic and the sentiment information for stance detection using multi-target data.

References

[1]

Pranav Anand, Marilyn A. Walker, Rob Abbott, Jean E. Fox Tree, Robeson Bowmani, and Michael Minor. 2011. Cats rule and dogs drool!: Classifying stance in online debate. In Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis. Association for Computational Linguistics, 1–9.

Digital Library

[2]

Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, and Kalina Bontcheva. 2016. Stance detection with bidirectional conditional encoding. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. The Association for Computational Linguistics, 876–885.

[3]

Luoxin Chen, Weitong Ruan, Xinyue Liu, and Jianhua Lu. 2020. SeqVAT: Virtual adversarial training for semi-supervised sequence labeling. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 8801–8811.

[4]

Sihao Chen, Daniel Khashabi, Wenpeng Yin, Chris Callison-Burch, and Dan Roth. 2019. Seeing things from a different angle: Discovering diverse perspectives about claims. In Proceedings of NAACL-HLT 2019. 542–557.

[5]

Weile Chen, Huiqiang Jiang, Qianhui Wu, Börje Karlsson, and Yi Guan. 2021. AdvPicker: Effectively leveraging unlabeled data via adversarial discriminator for cross-lingual NER. 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, 743–753.

[6]

Wei-Fan Chen and Lun-Wei Ku. 2016. UTCNN: A deep learning model of stance classification on social media text. In Proceedings of the 26th International Conference on Computational Linguistics. The Association for Computer Linguistics, 1635–1645.

[7]

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 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 4171–4186.

[8]

Sridhar Dhanya, Getoor Lise, and Walker Marilyn. 2014. Collective stance classification of posts in online debate forums. In Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media. 109–117.

[9]

Javid Ebrahimi, Dejing Dou, and Daniel Lowd. 2016. A joint sentiment-target-stance model for stance classification in tweets. In Proceedings of the 26th International Conference on Computational Linguistics. Association for Computational Linguistics, 2656–2665.

[10]

Abdellah El Mekki, Abdelkader El Mahdaouy, Ismail Berrada, and Ahmed Khoumsi. 2021. Domain adaptation for arabic cross-domain and cross-dialect sentiment analysis from contextualized word embedding. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 2824–2837.

[11]

William Fedus, Ian J. Goodfellow, and Andrew M. Dai. 2018. MaskGAN: Better text generation via filling in the ______. CoRR abs/1801.07736 (2018). arXiv:1801.07736 http://arxiv.org/abs/1801.07736.

[12]

Yujie Fu, Xiaoli Li, Yang Li, Suge Wang, Deyu Li, Jian Liao, and Jianxing Zheng. 2022. Incorporate opinion-towards for stance detection. Knowledge-Based Systems (2022), 108657.

Digital Library

[13]

Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor S. Lempitsky. 2016. Domain-adversarial training of neural networks. Machine Learning Research 17, 59 (2016), 1–35.

[14]

Kyle Glandt, Sarthak Khanal, Yingjie Li, Doina Caragea, and Cornelia Caragea. 2021. 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. Association for Computational Linguistics, 1596–1611.

[15]

Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of Advances in Neural Information Processing Systems. 2672–2680.

Digital Library

[16]

Tao Gui, Qi Zhang, Haoran Huang, Minlong Peng, and Xuanjing Huang. 2017. Part-of-speech tagging for Twitter with adversarial neural networks. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2411–2420.

[17]

Ivan Habernal and Iryna Gurevych. 2016. Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. The Association for Computer Linguistics, 1589–1599.

[18]

Momchil Hardalov, Arnav Arora, Preslav Nakov, and Isabelle Augenstein. 2021. Cross-domain label-adaptive stance detection. CoRR abs/2104.07467 (2021). arXiv:2104.07467 https://arxiv.53yu.com/abs/2104.07467.

[19]

Kazi Saidul Hasan and Vincent Ng. 2013. Stance classification of ideological debates: Data, models, features, and constraints. In Proceedings of the 6th International Joint Conference on Natural Language Processing. 1348–1356.

[20]

Kazi Saidul Hasan and Vincent Ng. 2014. Why are you taking this stance? Identifying and classifying reasons in ideological debates. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 751–762.

[21]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735–1780.

Digital Library

[22]

Marjan Hosseinia, Eduard C. Dragut, and Arjun Mukherjee. 2020. Stance prediction for contemporary issues: Data and experiments. In Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media, SocialNLP@ACL 2020, Online, July 10, 2020. Association for Computational Linguistics, 32–40.

[23]

Xuancheng Huang, Jingfang Xu, Maosong Sun, and Yang Liu. 2021. Transfer learning for sequence generation: From single-source to multi-source. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 5738–5750.

[24]

Sahil Jayaram and Emily Allaway. 2021. Human rationales as attribution priors for explainable stance detection. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 5540–5554.

[25]

Du Jiachen, Xu Ruifeng, He Yulan, and Gui Lin. 2017. Stance classification with target-specific neural attention networks. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 3988–3994.

[26]

Chang Li, Aldo Porco, and Dan Goldwasser. 2018. Structured representation learning for online debate stance prediction. In Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, 3728–3739.

[27]

Yingjie Li and Cornelia Caragea. 2019. 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. Association for Computational Linguistics, 6298–6304.

[28]

Yingjie Li and Cornelia Caragea. 2021. A multi-task learning framework for multi-target stance detection. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 2320–2326.

[29]

Yingjie Li and Cornelia Caragea. 2021. Target-aware data augmentation for stance detection. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 1850–1860.

[30]

Rui Liu, Zheng Lin, Peng Fu, Yuanxin Liu, and Weiping Wang. 2022. 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). IEEE, 7812–7816.

[31]

Xiaodong Liu, Jianfeng Gao, Xiaodong He, Li Deng, Kevin Duh, and Ye-Yi Wang. 2015. Representation learning using multi-task deep neural networks for semantic classification and information retrieval. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 912–921.

[32]

Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019. Multi-task deep neural networks for natural language understanding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 4487–4496.

[33]

Tomás Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of Advances in Neural Information Processing Systems. 3111–3119.

Digital Library

[34]

Takeru Miyato, Shin-Ichi Maeda, Masanori Koyama, and Shin Ishii. 2019. Virtual adversarial training: A regularization method for supervised and semi-supervised learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 41, 8 (2019), 1979–1993.

[35]

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. The Association for Computer Linguistics, 31–41.

[36]

Sinno Jialin Pan, Xiaochuan Ni, Jian-Tao Sun, Qiang Yang, and Zheng Chen. 2010. Cross-domain sentiment classification via spectral feature alignment. In Proceedings of the 19th International Conference on World Wide Web. ACM, 751–760.

Digital Library

[37]

Lis Pereira, Xiaodong Liu, Fei Cheng, Masayuki Asahara, and Ichiro Kobayashi. 2020. Adversarial training for commonsense inference. In Proceedings of the 5th Workshop on Representation Learning for NLP. 55–60.

[38]

Isaac Persing and Vincent Ng. 2016. Modeling stance in student essays. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. The Association for Computer Linguistics, 2174–2184.

[39]

Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR abs/1511.06434 (2015). http://arxiv.org/abs/1511.06434.

[40]

Benjamin Schiller, Johannes Daxenberger, and Iryna Gurevych. 2021. Stance detection benchmark: How robust is your stance detection? KI-Künstliche Intelligenz 35 (2021), 329–341.

[41]

Parinaz Sobhani, Saif Mohammad, and Svetlana Kiritchenko. 2016. Detecting stance in tweets and analyzing its interaction with sentiment. In Proceedings of the 5th Joint Conference on Lexical and Computational Semantics. 159–169.

[42]

Dhanya Sridhar, James R. Foulds, Bert Huang, Lise Getoor, and Marilyn A. Walker. 2015. Joint models of disagreement and stance in online debate. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing. The Association for Computer Linguistics, 116–125.

[43]

Somasundaran Swapna and Wiebe Janyce. 2010. Recognizing stances in ideological on-line debates. In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text. 116–124.

[44]

Matt Thomas, Bo Pang, and Lillian Lee. 2006. Get out the vote: Determining support or opposition from Congressional floor-debate transcripts. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 327–335.

Digital Library

[45]

Zhongqing Wang, Qingying Sun, Shoushan Li, Qiaoming Zhu, and Guodong Zhou. 2020. Neural stance detection with hierarchical linguistic representations. IEEE ACM Trans. Audio Speech Lang. Process. 28 (2020), 635–645.

Digital Library

[46]

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

[47]

Wan Wei, Xiao Zhang, Xuqin Liu, Wei Chen, and Tengjiao Wang. 2016. pkudblab at SemEval-2016 task 6 : A specific convolutional neural network system for effective stance detection. In Proceedings of the 10th International Workshop on Semantic Evaluation. The Association for Computer Linguistics, 384–388.

[48]

Chang Xu, Cécile 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. Association for Computational Linguistics, 778–783.

[49]

Chang Xu, Cécile Paris, Surya Nepal, and Ross Sparks. 2019. Recognising agreement and disagreement between stances with reason comparing networks. In Proceedings of the 57th Conference of the Association for Computational Linguistics. Association for Computational Linguistics, 4665–4671.

[50]

Guido Zarrella and Amy Marsh. 2016. MITRE at SemEval-2016 task 6: Transfer learning for stance detection. In Proceedings of the 10th International Workshop on Semantic Evaluation. The Association for Computer Linguistics, 458–463.

[51]

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. Association for Computational Linguistics, 3188–3197.

[52]

Xin Zhang, Guangwei Xu, Yueheng Sun, Meishan Zhang, and Pengjun Xie. 2021. Crowdsourcing learning as domain adaptation: A case study on named entity recognition. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, 5558–5570.

[53]

Yftah Ziser and Roi Reichart. 2017. Neural structural correspondence learning for domain adaptation. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL’17). Association for Computational Linguistics, 400–410.

[54]

Bowei Zou, Zengzhuang Xu, Yu Hong, and Guodong Zhou. 2018. Adversarial feature adaptation for cross-lingual relation classification. In Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, 437–448.

Cited By

Alturayeif NLuqman HAhmed M(2023)Enhancing stance detection through sequential weighted multi-task learningSocial Network Analysis and Mining10.1007/s13278-023-01169-714:1Online publication date: 9-Dec-2023
https://doi.org/10.1007/s13278-023-01169-7
Alturayeif NLuqman HAhmed M(2023)A systematic review of machine learning techniques for stance detection and its applicationsNeural Computing and Applications10.1007/s00521-023-08285-735:7(5113-5144)Online publication date: 28-Jan-2023
https://dl.acm.org/doi/10.1007/s00521-023-08285-7

Index Terms

Stance Detection with a Multi-Target Adversarial Attention Network
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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, ...
Stance detection via sentiment information and neural network model

Stance detection aims to automatically determine whether the author is in favor of or against a given target. In principle, the sentiment information of a post highly influences the stance. In this study, we aim to leverage the sentiment information of ...
Leveraging relevant summarized information and multi-layer classification to generalize the detection of misleading headlines
Abstract
Disinformation is an important problem facing society nowadays. Given the rapid and easy access to information, news stories quickly go viral, the vast majority of which are misleading and with no prospect of verification. Specifically, the ...
Highlights
- HeadlineStanceChecker+ is an enhanced system for misleading headlines detection.
- Misleading headlines detection is tackled as a stance detection problem.
- Summarization supports stance detection by only selecting relevant ...

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 2

February 2023

624 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3572719

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 ACM 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: 27 December 2022

Online AM: 16 June 2022

Accepted: 06 June 2022

Revised: 15 May 2022

Received: 10 September 2021

Published in TALLIP Volume 22, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Funding Sources

National Natural Science Foundation of China
Qing Lan Project of Jiangsu Universities, Opening Foundation of Jiangsu Big Data Intelligent Engineering Laboratory of Soochow University
Project of Natural Science Research of Huai.an

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
691
Total Downloads

Downloads (Last 12 months)184
Downloads (Last 6 weeks)15

Reflects downloads up to 22 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Alturayeif NLuqman HAhmed M(2023)Enhancing stance detection through sequential weighted multi-task learningSocial Network Analysis and Mining10.1007/s13278-023-01169-714:1Online publication date: 9-Dec-2023
https://doi.org/10.1007/s13278-023-01169-7
Alturayeif NLuqman HAhmed M(2023)A systematic review of machine learning techniques for stance detection and its applicationsNeural Computing and Applications10.1007/s00521-023-08285-735:7(5113-5144)Online publication date: 28-Jan-2023
https://dl.acm.org/doi/10.1007/s00521-023-08285-7

View Options

Get Access

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.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents