Using Context Information to Enhance Simple Question Answering

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., a pipeline framework, an end-to-end framework) to focus on answering single-relation factoid questions. 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 pipeline framework, it includes two cascaded steps: entity detection and relation detection. 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.