Dynamic Multiple-level Semantic Extraction Model Based on External Knowledge
JIANG Wenchao1,2, ZHUANG Zhigang1, TU Xuping2, LI Chuanjie3, LIU Haibo1,
1.School of Computers, Guangdong University of Technology,Guangzhou 510006; 2.Guangdong Electronics Industry Institute, Dongguan 523808; 3.Guangdong Guangxin Communications Services Company Ltd., Guangzhou 510630
Abstract：To resolve the problems of semantic understanding and answer extraction in complex multiple context machine reading comprehension environments, a dynamic multiple-level semantic extraction model based on external knowledge is presented. Firstly, the optimized gated single cyclic neural network model is utilized to match the text as well as the problem set. Then, the dynamic multiple-dimension bidirectional attention mechanism analysis is implemented on the text and the problem set respectively to improve the semantic matching precision. Next, a dynamic pointer network is utilized to determine the rank of the answers to the questions. Finally, the candidate answers are sorted based on external knowledge and experiences, and the precision of the final answer is improved further. The experimental results show that problem-answer matching accuracy of the proposed model is significantly improved compared with the mainstream models. Furthermore, the proposed model obtains higher robustness in complex reading comprehension tasks in different application scenes.
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2019, Vol. 32 
