Yanzeng Li's paper titled "CORD: A Three-Stage Coarse-to-Fine Framework for Relation Detection in Knowledge Base Question Answering" has been accepted by CIKM 2023. 
As a fundamental subtask of Knowledge Base Question Answering (KBQA), Relation Detection (KBQA-RD) plays a crucial role to detect the KB relations between entities or variables in natural language questions. It remains, however, a challenging task, particularly for significant large-scale relations and in the presence of easily confused relations. Recent state-of-the-art methods not only struggle with such scenarios, but often take into account only one facet and fail to incorporate the subtle discrepancy among the relations. In this paper, we propose a simple and efficient three-stage framework to exploit the coarse-to-fine paradigm. Specifically, we employ a natural clustering over all KB relations and perform a coarse-to-fine relation recognition process based on the relation clustering. In this way, our framework refines the detection of relations, so as to scale well with large-scale relations. Experiments on both single-relation (SimpleQuestions) and multi-relation (WebQSP) benchmarks show that our method not only achieves the outstanding relation detection performance in KBQA-RD subtask, but also effectively improves the overall accuracy of KBQA systems.