多模态多轮对话立场检测：一个挑战性数据集与有效模型
Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model
AI总结：
针对现有多模态立场检测忽略多方对话语境的问题，本文提出多模态多轮对话立场检测数据集MmMtCSD及多模态大语言模型框架MLLM-SD，通过联合学习文本与视觉表示实现最先进的立场检测性能。
摘要：
立场检测旨在利用社交媒体数据识别公众对特定目标的态度，是一项重要但具有挑战性的任务。随着包含文本和图像在内的多样化多模态社交媒体内容的激增，多模态立场检测（MSD）已成为一个关键研究领域。然而，现有的MSD研究集中于对单个文本-图像对中的立场进行建模，忽视了社交媒体上自然发生的多方对话语境。这一局限源于缺乏能够真实捕捉此类对话场景的数据集，阻碍了对话式MSD的进展。为解决这一问题，我们引入了一个新的多模态多轮对话立场检测数据集（称为MmMtCSD）。为了从这一具有挑战性的数据集中推导立场，我们提出了一种新颖的多模态大语言模型立场检测框架（MLLM-SD），该框架从文本和视觉模态中学习联合立场表示。在MmMtCSD上的实验表明，我们提出的MLLM-SD方法在多模态立场检测中取得了最先进的性能。我们相信，MmMtCSD将有助于推动立场检测研究在现实世界中的应用。
英文摘要：
Stance detection, which aims to identify public opinion towards specific targets using social media data, is an important yet challenging task. With the proliferation of diverse multimodal social media content including text, and images multimodal stance detection (MSD) has become a crucial research area. However, existing MSD studies have focused on modeling stance within individual text-image pairs, overlooking the multi-party conversational contexts that naturally occur on social media. This limitation stems from a lack of datasets that authentically capture such conversational scenarios, hindering progress in conversational MSD. To address this, we introduce a new multimodal multi-turn conversational stance detection dataset (called MmMtCSD). To derive stances from this challenging dataset, we propose a novel multimodal large language model stance detection framework (MLLM-SD), that learns joint stance representations from textual and visual modalities. Experiments on MmMtCSD show state-of-the-art performance of our proposed MLLM-SD approach for multimodal stance detection. We believe that MmMtCSD will contribute to advancing real-world applications of stance detection research.
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。
