结合外部知识的动态多层次语义抽取网络模型

针对复杂多文本机器阅读理解任务中的语义理解与答案提取问题,提出结合外部知识的动态多层次语义理解与答案抽取模型.首先利用改进的门控单元循环神经网络匹配文本内容与问题集,分别在向量化文本内容及问题集上实施多维度动态双向注意力机制分析,提高语义匹配精度.然后利用动态指针网络确定问题答案范围,改进网络模型语义匹配效率,降低答案提取冗余度.最后结合外部知识与经验改进候选答案精准性排序,得到最终答案.实验表明文中模型的语义匹配与答案提取精度显著提升,对不同领域的复杂文本阅读理解任务具有较高的鲁棒性.

Dynamic Multiple-level Semantic Extraction Model Based on External Knowledge

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.