基于多粒度知识的无监督常识问答OA

As a natural language understanding task,commonsense question answering(CQA)is signifi-cantly more challenging than conventional question answering tasks.It requires the model to possess stron-ger commonsense reasoning capabilities.Currently,unsupervised methods for CQA have achieved rela-tively good performance on several datasets,but these approaches struggle to adequately mine and utilize commonsense knowledge,limiting the model's reasoning ability in complex scenarios.To address this issue,this paper proposed a novel unsupervised CQA method,whose core advantage lay in effectively integrating external commonsense knowledge through unsupervised learning,thereby enhancing the model's generalization capability and reasoning depth.Firstly,the method classifies questions into sci-entific commonsense questions and everyday event questions.Then,it generates corresponding knowledge prefixes based on the question type.Next,these knowledge prefixes are input into a pre-trained language model to produce multi-granularities commonsense knowledge through large model prompts.Finally,the multi-grained knowledge is leveraged to assist the answer generation module in reasoning.The adoption of an unsupervised approach not only reduces the reliance on annotated data but also better adapts to diverse commonsense scenarios,demonstrating its flexibility and generalizability in practical applications.Experimental results show that the proposed method significantly outperforms baseline models on relevant datasets,validating its correctness and rationality in unsupervised CQA tasks.