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

• URL: http://arxiv.org/abs/2107.01592v1
• Date: Sun, 4 Jul 2021 10:56:36 GMT
• Title: Coarse-to-Careful: Seeking Semantic-related Knowledge for Open-domain Commonsense Question Answering
• Authors: Luxi Xing, Yue Hu, Jing Yu, Yuqiang Xie, Wei Peng
• Abstract summary: It is prevalent to utilize external knowledge to help machine answer questions that need background commonsense. We propose a semantic-driven knowledge-aware QA framework, which controls the knowledge injection in a coarse-to-careful fashion.
• Score: 12.406729445165857
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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