南京大学学报(自然科学版) ›› 2019, Vol. 55 ›› Issue (1): 125–132.doi: 10.13232/j.cnki.jnju.2019.01.013
顾健伟1,2，曾 诚3，邹恩岑1，陈 扬1,2，沈 艺1,2，陆 悠1，奚雪峰1,2*
Gu Jianwei1,2，Zeng Cheng3，Zou Encen1，Chen Yang1,2，Shen Yi1,2，Lu You1，Xi Xuefeng1,2*
摘要： 机器阅读理解(Machine Reading Comprehension，MRC)一直是自然语言处理(Natural Language Processing，NLP)领域的研究热点和核心问题. 近期，百度开源了一款大型中文阅读理解数据集DuReader，旨在处理现实生活中的RC(Reading Comprehension)问题. 该数据集包含1000 k的文本、200 k的问题和420 k的答案，是目前最大型的中文机器阅读理解数据集，在此数据集上发布的阅读理解任务比以往更具有实际意义，也更有难度. 针对该数据集的阅读理解任务，分析研究了一种结合双向注意力流与自注意力(self－attention)机制实现的神经网络模型. 该模型通过双向注意力流机制来获取query－aware上下文信息表征并进行粒度分级，使用自注意力机制捕捉文本和问题句内的词语依赖关系和句法信息，再通过双向长短期记忆(Long Short－Term Memory，LSTM)网络进行语义信息聚合. 实验结果最终得到相同词数百分比(BLEU－4)为44.7%，重叠单元百分比(Rouge－L)为49.1%，与人类测试平均水平较为接近，证明了该模型的有效性．
中图分类号:
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