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专注于评估单个实例中的立场，因此在有效建模真实社交媒体互动中自然发生的、针对同一特定主题的多方讨论方面存在局限。这一限制主要源于缺乏能真实再现社交媒体场景的数据集，从而阻碍了对话立场检测的研究进展。本文提出一个新的多轮对话立场检测数据集（命名为**MT-CSD**），该数据集包含多个用于对话立场检测的目标。为应对这一具有挑战性数据集中的立场推断，我们提出一种全局-局部注意力网络（**GLAN**），以处理对话数据中固有的长程与短程依赖关系。值得注意的是，即使以GLAN为代表的最先进立场检测方法，其准确率也仅为50.47%，凸显了对话立场检测中持续的挑战。此外，我们的MT-CSD数据集作为宝贵资源，可推动跨领域立场检测的进展——在此类任务中，分类器需从不同但相关的目标迁移适应。我们相信，MT-CSD将有助于推进立场检测研究的实际应用。我们的源代码、数据及模型已公开于\url{https://github.com/nfq729/MT-CSD}。