Using Context Information to Enhance Simple Question Answering
This work addresses the challenge of enhancing accuracy and efficiency in knowledge base question answering, which is incremental as it builds on existing methods by adding context.
The paper tackles the problem of improving simple question answering on knowledge bases by incorporating context information like entity types and out-degree, showing that both pipeline and end-to-end frameworks achieve better results, with the end-to-end framework being competitive in accuracy and faster than state-of-the-art methods.
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.