Cheng Xu


2018

pdf
The APVA-TURBO Approach To Question Answering in Knowledge Base
Yue Wang | Richong Zhang | Cheng Xu | Yongyi Mao
Proceedings of the 27th International Conference on Computational Linguistics

In this paper, we study the problem of question answering over knowledge base. We identify that the primary bottleneck in this problem is the difficulty in accurately predicting the relations connecting the subject entity to the object entities. We advocate a new model architecture, APVA, which includes a verification mechanism responsible for checking the correctness of predicted relations. The APVA framework naturally supports a well-principled iterative training procedure, which we call turbo training. We demonstrate via experiments that the APVA-TUBRO approach drastically improves the question answering performance.