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  • SPS
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    Length: 00:12:30
11 Jun 2021

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 question in a hierarchical way. Experiment results illustrate that the proposed approach promotes the performance on the CommonsenseQA dataset comparing with strong baselines.

Chairs:
Kai Yu

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