Stance detection method based on knowledge augmentation and adversarial training

HUANG Liyuan; ZHANG Jin; JIN Xiaolong; XU Hui; GUO Jiafeng

doi:10.11959/j.issn.2096-0271.25261

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Stance detection method based on knowledge augmentation and adversarial training

更新时间：2026-02-27

- Stance detection method based on knowledge augmentation and adversarial training
- BIG DATA RESEARCH Pages: 1-13(2026)
- 作者机构：
  
  1.中国科学院计算技术研究所网络数据科学与技术重点实验室，北京 100190
  2.智能算法安全全国重点实验室，北京 100190
  3.中国科学院大学计算机科学与技术学院，北京 101408
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.25261
  CLC： TP391.1
- Received：30 November 2025，
  
  Online First：26 January 2026，
- 稿件说明：
移动端阅览
黄莉媛,张瑾,靳小龙等.基于知识增强和对抗训练的立场检测方法[J].大数据,

HUANG Liyuan,ZHANG Jin,JIN Xiaolong,et al.Stance detection method based on knowledge augmentation and adversarial training[J].BIG DATA RESEARCH,
黄莉媛,张瑾,靳小龙等.基于知识增强和对抗训练的立场检测方法[J].大数据, DOI：10.11959/j.issn.2096-0271.25261.

HUANG Liyuan,ZHANG Jin,JIN Xiaolong,et al.Stance detection method based on knowledge augmentation and adversarial training[J].BIG DATA RESEARCH, DOI：10.11959/j.issn.2096-0271.25261.

摘要

立场检测是自然语言处理领域的一个重要研究方向，旨在判断作者对特定目标所持有的支持、反对或中立态度。针对社交媒体场景中文本语言复杂、领域知识匮乏和模型泛化能力弱的问题，提出了一种基于知识增强和对抗训练的文本立场检测方法KABERT。方法结合生成式与判别式模型的优势，先使用生成式模型从关键词、隐含情感和修辞手法角度提取文本与目标之间的深层语义关系，生成隐含知识；再使用判别式模型作为主干分类网络进行有监督微调，并引入快速梯度法进行对抗训练。在两个标准立场检测数据集SEM16和P-Stance上的实验结果显示，所提方法的Macro-F1分别为68.85%和79.24%，较目前主流方法均有不同程度的提升，证明了所提方法的有效性。

Abstract

Stance detection is a crucial research direction in natural language processing

aiming to determine the author's supportive

opposing

or neutral attitude towards a specific target. To address challenges such as complex linguistic expressions

insufficient domain-specific knowledge

and weak model generalization in social media contexts

this paper proposed a stance detection method named KABERT

based on knowledge augmentation and adversarial training. The method integrated the advantages of generative and discriminative models. A generative model was first used to extract deep semantic relationships between text and the target from the perspectives of keywords

implicit sentiment

and rhetorical devices

generating implicit knowledge. Then

a discriminative model was used as the classification backbone network for supervised fine-tuning

and a fast gradient method was introduced for adversarial training. Experiments were conducted on two standard stance detection datasets

SEM16 and P-Stance. Results show that KABERT achieves Macro-F1 scores of 68.85% and 79.24% respectively

outperforming current mainstream approaches by varying margins

demonstrating the effectiveness of the proposed approach.

关键词

Keywords

references

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