# Using Context Information to Enhance Simple Question Answering

arXiv: https://arxiv.org/abs/1905.01995 · HTML: https://mlsanity.com/paper/1905.01995/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2019.

## Built on by (most cited first, 1 of 1)

- [Comparative Analysis of 47 Context-Based Question Answer Models Across 8 Diverse Datasets](https://mlsanity.com/paper/2512.00323/) (2025)

## Abstract (arXiv)

With the rapid development of knowledge bases(KBs),question answering(QA)based on KBs has become a hot research issue. In this paper,we propose two frameworks(i.e.,pipeline framework,an end-to-end framework)to focus answering single-relation factoid question. In both of two frameworks,we study the effect of context information on the quality of QA,such as the entity's notable type,out-degree. In the end-to-end framework,we combine char-level encoding and self-attention mechanisms,using weight sharing and multi-task strategies to enhance the accuracy of QA. Experimental results show that context information can get better results of simple QA whether it is the pipeline framework or the end-to-end framework. In addition,we find that the end-to-end framework achieves results competitive with state-of-the-art approaches in terms of accuracy and take much shorter time than them.