A Gated Dilated Convolution with Attention Model for Clinical Cloze-Style Reading Comprehension

被引：11

作者：

Wang, Bin ^{[1
]}

Zhang, Xuejie ^{[1
]}

Zhou, Xiaobing ^{[1
]}

Li, Junyi ^{[1
]}

机构：

[1] Yunnan Univ, Sch Informat Sci & Engn, Kunming 650091, Yunnan, Peoples R China

来源：

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH | 2020年 / 17卷 / 04期

关键词：

clinical medicine; machine reading comprehension; cloze-style; Gated Dilated Convolution; attention mechanism;

D O I：

10.3390/ijerph17041323

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The machine comprehension research of clinical medicine has great potential value in practical application, but it has not received sufficient attention and many existing models are very time consuming for the cloze-style machine reading comprehension. In this paper, we study the cloze-style machine reading comprehension in the clinical medical field and propose a Gated Dilated Convolution with Attention (GDCA) model, which consists of a gated dilated convolution module and an attention mechanism. Our model has high parallelism and is capable of capturing long-distance dependencies. On the CliCR data set, our model surpasses the present best model on several metrics and obtains state-of-the-art result, and the training speed is 8 times faster than that of the best model.

引用

页数：11