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