Enhancing Argumentative Relation Classification by Multi-Granularity Retrieval and Heterogeneous Graph Reasoning

Caihua Yang, Jianzhu Bao, Bin Liang, Ruifeng Xu

Abstract: Argumentative relation classification (ARC) aims to identify the relation between arguments. Previous methods that employ structured knowledge graphs to tackle the ARC task have achieved promising results. However, the prerequisite for structured knowledge to function is that the knowledge includes the topics of arguments. In practice, the topics of arguments are constantly emerging, making it impractical to construct a structured knowledge graph that contains all potential topics in advance. To address this issue, we investigate ARC from a novel perspective by utilizing unstructured knowledge to enhance the learning of ARC, where useful information for topics and arguments could be flexibly obtained from unstructured knowledge. Specifically, to retrieve diverse and comprehensive knowledge for topics and arguments, we first propose a multi-granularity retrieval method tailored for ARC, which acquires unstructured knowledge by dense retrieval at three levels of granularity: the concept level, the concept relation level, and the argument level. Further, we introduce a Knowledge-aware Heterogeneous Graph Reasoner (KHGR), which enables better utilization of retrieved knowledge to facilitate ARC. Extensive experiments on three publicly available datasets verify the superiority of our model compared with several state-of-the-art baselines. Further analysis shows that our method yields more significant benefits in low-resource scenarios.