# Using Context Information to Enhance Simple Question Answering

## TL;DR

This paper explores how incorporating context information improves simple question answering over knowledge bases, proposing two frameworks and demonstrating enhanced accuracy and efficiency.

## Contribution

It introduces two frameworks for single-relation factoid question answering that leverage context information and combines advanced encoding techniques in an end-to-end model.

## Key findings

- Context information improves QA accuracy in both frameworks.
- End-to-end framework achieves competitive results with less time.
- Combining char-level encoding and self-attention enhances QA performance.

## Abstract

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.