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

Source: [Coarse-to-Careful: Seeking Semantic-related Knowledge for Open-domain Commonsense Question Answering](<https://www.sota2.com/research/paper/68fa6a431e209131cea271e9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:20:37.043Z

Authors: Luxi Xing, Yue Hu, Jing Yu, Yuqiang Xie, Wei Peng

Published: 2021-07-04T10:56:36.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2107.01592>)

[PDF](<https://arxiv.org/pdf/2107.01592.pdf>)