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.
@article{arxiv.2107.01592,
title = {Coarse-to-Careful: Seeking Semantic-related Knowledge for Open-domain Commonsense Question Answering},
author = {Luxi Xing and Yue Hu and Jing Yu and Yuqiang Xie and Wei Peng},
journal= {arXiv preprint arXiv:2107.01592},
year = {2021}
}