发表机构
School of Software Engineering, Xi’an Jiaotong University; College of Computer Science and Technology, Xi’an University of Electronic Science and Technology; Nexus Lab, Peking University; Computer Science and Technology, Xi’an Jiaotong University; School of education, Zhejiang University; Academy of Mathematics and Systems Science, Chinese Academy of Sciences; National Engineering Laboratory for Big Data Analysis and Applications, Peking University（西安交通大学软件学院; 西安电子科技大学计算机科学与技术学院; 北京大学Nexus实验室; 西安交通大学计算机科学与技术; 浙江大学教育学院; 中国科学院数学与系统科学研究院; 北京大学大数据分析与应用技术国家工程实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多模态聊天分析中情感与意图识别相互干扰的问题，提出ExCoVer框架，通过情感先验思维链和可学习言语化规则，在CSMSA和MSAIRS数据集上取得最优性能并提供显式推理链。
AI 中文摘要
多模态聊天中的社交媒体贴纸分析（MCAS）受益于对文本和贴纸语义的联合建模，但其本质上受到情感识别与意图识别之间相互干扰的挑战。尽管现有的多任务方法取得了有竞争力的性能，但它们大多忽略了这种任务间干扰，并且对于这两种预测是如何做出的几乎没有提供明确的推理。为了解决这个问题，我们提出了ExCoVer，一个带有言语化规则学习的显式思维链框架，该框架将情感先验推理与可学习的判别规则相结合，为情感和意图预测生成显式推理链。具体来说，ExCoVer由两个组件组成：（1）情感先验思维链（SP-CoT），它检测跨模态情感冲突，并使用主导情感作为先验来减轻任务间干扰并缩小候选意图空间；（2）用于混淆意图判别的言语化规则学习（VRLCID），它将判别规则视为可学习参数，并通过学习者、优化器和正则化器代理对其进行优化，以抑制虚假相关性并区分混淆意图。在CSMSA和MSAIRS数据集上的大量实验表明，ExCoVer在提供显式推理链的同时实现了最先进的性能。
英文摘要
Multimodal chat analysis of social media stickers (MCAS) benefits from jointly modeling text and sticker semantics, yet it is inherently challenged by the interference between sentiment and intent recognition. Although existing multi-task approaches achieve competitive performance, they largely ignore this inter-task interference and offer little explicit reasoning about how these two predictions are made. To address this issue, we propose \textbf{ExCoVer}, an \textbf{Ex}plicit \textbf{C}hain-\textbf{o}f-Thought framework with \textbf{Ver}balized rules learning that integrates sentiment-prior reasoning with learnable discrimination rules to produce explicit reasoning chains for sentiment and intent predictions. Specifically, ExCoVer consists of two components: (1) Sentiment-Prior Chain-of-Thought (SP-CoT), which detects cross-modal sentiment conflicts and uses the dominant sentiment as a prior to mitigate inter-task interference and narrow the candidate intent space; and (2) Verbalized Rules Learning for Confusing Intent Discrimination (VRLCID), which treats discrimination rules as learnable parameters and optimizes them via learner, optimizer, and regularizer agents to suppress spurious correlations and distinguish confusing intents. Extensive experiments on CSMSA and MSAIRS datasets demonstrate that ExCoVer achieves state-of-the-art performance while providing explicit reasoning chains.
Comments14 pages,7 figures,
