A Challenge Dataset and Effective Models for Conversational Stance Detection
arxiv(2024)
摘要
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 MT-CSD), which encompasses multiple
targets for conversational stance detection. To derive stances from this
challenging dataset, we propose a global-local attention network
(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 .
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要