Improve Diverse Commonsense Generation by Enhancing Subgraphs

Commonsense reasoning (CSR) requires rationale beyond the explicit knowledge mentioned in the context. Many existing methods use knowledge graphs (KGs) to generate rationale as additional evidence for CSR. However, rationale extracted from KGs (e.g., ConceptNet) often includes irrelevant information, which easily introduces noise and affects the evidential quality generated. Similar to brainstorming to generate diverse ideas, we introduce a synonym expansion method to expand input concepts, ultimately constructing a task relevant knowledge subgraph. Additionally, we propose a pruning model that learns to score and prune the knowledge subgraph, removing parts that are not directly related to the input context. The proposed method improves the quality and diversity of rationale, which benefits generative commonsense reasoning tasks. Experiments on two datasets validated the effectiveness of our method, which demonstrates comparable performance with existing methods.