Previous stance detection studies typically concentrate on evaluating stances within individual instances, thereby exhibiting limitations in effectively modeling multi-party discussions concerning the same specific topic, as naturally transpire in authentic social media interactions. This constraint arises primarily due to the scarcity of datasets that authentically replicate real social media contexts, hindering the research progress of conversational stance detection. In this paper, we introduce a new multi-turn conversation stance detection dataset (called \textbf{MT-CSD}), which encompasses multiple targets for conversational stance detection. To derive stances from this challenging dataset, we propose a global-local attention network (\textbf{GLAN}) to address both long and short-range dependencies inherent in conversational data. Notably, even state-of-the-art stance detection methods, exemplified by GLAN, exhibit an accuracy of only 50.47\%, highlighting the persistent challenges in conversational stance detection. Furthermore, our MT-CSD dataset serves as a valuable resource to catalyze advancements in cross-domain stance detection, where a classifier is adapted from a different yet related target. We believe that MT-CSD will contribute to advancing real-world applications of stance detection research. Our source code, data, and models are available at \url{https://github.com/nfq729/MT-CSD}.

翻译：以往的立场检测研究通常集中于评估单个实例中的立场，因此在有效模拟真实社交媒体互动中自然发生的、围绕同一特定话题的多方讨论方面存在局限性。这种约束主要源于缺乏能真实再现社交媒体语境的数据集，阻碍了对话式立场检测的研究进展。在本文中，我们引入了一个新的多轮对话立场检测数据集（称为\textbf{MT-CSD}），该数据集涵盖了对话立场检测的多个目标。为从这一具有挑战性的数据集中推断立场，我们提出了一种全局-局部注意力网络（\textbf{GLAN}），以处理对话数据中固有的长程和短程依赖关系。值得注意的是，即使是以GLAN为代表的先进立场检测方法，其准确率也仅为50.47%，凸显了对话式立场检测中持续存在的挑战。此外，我们的MT-CSD数据集可作为宝贵资源，推动跨领域立场检测的进步——在该场景中，分类器需从不同但相关的目标迁移适应。我们相信，MT-CSD将有助于推动立场检测研究的实际应用。我们的源代码、数据和模型可在\url{https://github.com/nfq729/MT-CSD}获取。