Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach
Zero-shot stance detection aims to identify the stance expressed in social media text aimed at specific targets without relying on annotated data. However, due to insufficient contextual information and the inherent ambiguity of language, this task faces numerous challenges in low-resource scenarios...
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/4612 |
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| author | Junxia Ma Changjiang Wang Lu Rong Bo Wang Yaoli Xu |
| author_facet | Junxia Ma Changjiang Wang Lu Rong Bo Wang Yaoli Xu |
| author_sort | Junxia Ma |
| collection | DOAJ |
| description | Zero-shot stance detection aims to identify the stance expressed in social media text aimed at specific targets without relying on annotated data. However, due to insufficient contextual information and the inherent ambiguity of language, this task faces numerous challenges in low-resource scenarios. This work proposes a novel zero-shot stance detection method based on multi-agent debate (ZSMD) to address the aforementioned challenges. Specifically, we construct two debater agents representing the supporting and opposing stances. A knowledge enhancement module supplements the original tweet and target with relevant background knowledge, providing richer contextual support for argument generation. Subsequently, the two agents engage in debate over a predetermined number of rounds, employing rebuttal strategies such as factual verification, logical analysis, and sentiment analysis. If no consensus is reached within the specified rounds, a referee agent synthesizes the debate process and original input information to deliver the final stance determination. We evaluate ZSMD on two benchmark datasets, SemEval-2016 Task 6 and P-Stance, and compare it against strong zero-shot baselines such as MB-Cal and COLA. The experimental results show that ZSMD not only achieves higher accuracy than these baselines, but also provides deeper insights into subtle differences in opinion expression, highlighting the potential of structured argumentation in low-resource settings. |
| format | Article |
| id | doaj-art-2acaf1d5edba45769db8c3b1b37e3e65 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2acaf1d5edba45769db8c3b1b37e3e652025-08-20T03:52:57ZengMDPI AGApplied Sciences2076-34172025-04-01159461210.3390/app15094612Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel ApproachJunxia Ma0Changjiang Wang1Lu Rong2Bo Wang3Yaoli Xu4College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaCollege of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaSchool of Education, Tianjin University, Tianjin 300072, ChinaCollege of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaCollege of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaZero-shot stance detection aims to identify the stance expressed in social media text aimed at specific targets without relying on annotated data. However, due to insufficient contextual information and the inherent ambiguity of language, this task faces numerous challenges in low-resource scenarios. This work proposes a novel zero-shot stance detection method based on multi-agent debate (ZSMD) to address the aforementioned challenges. Specifically, we construct two debater agents representing the supporting and opposing stances. A knowledge enhancement module supplements the original tweet and target with relevant background knowledge, providing richer contextual support for argument generation. Subsequently, the two agents engage in debate over a predetermined number of rounds, employing rebuttal strategies such as factual verification, logical analysis, and sentiment analysis. If no consensus is reached within the specified rounds, a referee agent synthesizes the debate process and original input information to deliver the final stance determination. We evaluate ZSMD on two benchmark datasets, SemEval-2016 Task 6 and P-Stance, and compare it against strong zero-shot baselines such as MB-Cal and COLA. The experimental results show that ZSMD not only achieves higher accuracy than these baselines, but also provides deeper insights into subtle differences in opinion expression, highlighting the potential of structured argumentation in low-resource settings.https://www.mdpi.com/2076-3417/15/9/4612zero-shot stance detectionmulti-agent debateknowledge augmentZSMD |
| spellingShingle | Junxia Ma Changjiang Wang Lu Rong Bo Wang Yaoli Xu Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach Applied Sciences zero-shot stance detection multi-agent debate knowledge augment ZSMD |
| title | Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach |
| title_full | Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach |
| title_fullStr | Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach |
| title_full_unstemmed | Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach |
| title_short | Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach |
| title_sort | exploring multi agent debate for zero shot stance detection a novel approach |
| topic | zero-shot stance detection multi-agent debate knowledge augment ZSMD |
| url | https://www.mdpi.com/2076-3417/15/9/4612 |
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