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It is prevalent to utilize external knowledge to help machine answerquestions that need background commonsense, which faces a problem thatunlimited knowledge will transmit noisy and misleading information. Towards theissue of introducing related knowledge, we propose a semantic-drivenknowledge-aware QA framework, which controls the knowledge injection in acoarse-to-careful fashion. We devise a tailoring strategy to filter extractedknowledge under monitoring of the coarse semantic of question on the knowledgeextraction stage. And we develop a semantic-aware knowledge fetching modulethat engages structural knowledge information and fuses proper knowledgeaccording to the careful semantic of questions in a hierarchical way.Experiments demonstrate that the proposed approach promotes the performance onthe CommonsenseQA dataset comparing with strong baselines.

Coarse-to-Careful: Seeking Semantic-related Knowledge for Open-domain Commonsense Question Answering