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arXiv 2512.04492cs.CL
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MSME: 一种多阶段多专家框架用于零样本立场检测

MSME: A Multi-Stage Multi-Expert Framework for Zero-Shot Stance Detection

footnotemark: 1(机构1)

Yuanshuo Zhang, Aohua Li, Bo Chen, Jingbo Sun, Xiaobing Zhao

AI总结:

MSME提出了一种多阶段多专家框架,通过知识准备、专家推理和决策聚合三个阶段,提升零样本立场检测的准确性和鲁棒性。

摘要:

基于大语言模型的方法最近在零样本立场检测中取得了令人印象深刻的结果。然而,它们仍然在复杂的现实场景中挣扎,其中立场理解需要动态的背景知识,目标定义涉及复合实体或事件,必须明确链接到立场标签,而修辞手法如讽刺往往会掩盖作者的真实意图。为了解决这些挑战,我们提出了MSME,一种用于零样本立场检测的多阶段多专家框架。MSME包括三个阶段:(1)知识准备,其中相关背景知识被检索并澄清立场标签;(2)专家推理,包括三个专门模块——知识专家从知识角度提炼显著事实和原因,标签专家相应地细化立场标签和原因,语用专家检测讽刺等修辞线索,从语用角度推断意图;(3)决策聚合,其中元判断者整合所有专家分析以产生最终的立场预测。在三个公开数据集上的实验表明,MSME在各方面都实现了最先进的性能。

英文摘要:

LLM-based approaches have recently achieved impressive results in zero-shot stance detection. However, they still struggle in complex real-world scenarios, where stance understanding requires dynamic background knowledge, target definitions involve compound entities or events that must be explicitly linked to stance labels, and rhetorical devices such as irony often obscure the author's actual intent. To address these challenges, we propose MSME, a Multi-Stage, Multi-Expert framework for zero-shot stance detection. MSME consists of three stages: (1) Knowledge Preparation, where relevant background knowledge is retrieved and stance labels are clarified; (2) Expert Reasoning, involving three specialized modules-Knowledge Expert distills salient facts and reasons from a knowledge perspective, Label Expert refines stance labels and reasons accordingly, and Pragmatic Expert detects rhetorical cues such as irony to infer intent from a pragmatic angle; (3) Decision Aggregation, where a Meta-Judge integrates all expert analyses to produce the final stance prediction. Experiments on three public datasets show that MSME achieves state-of-the-art performance across the board.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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