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

Author

Listed:
  • Bin Wang

    (School of Information Science and Engineering, Yunnan University, Kunming 650091, China)

  • Xuejie Zhang

    (School of Information Science and Engineering, Yunnan University, Kunming 650091, China)

  • Xiaobing Zhou

    (School of Information Science and Engineering, Yunnan University, Kunming 650091, China)

  • Junyi Li

    (School of Information Science and Engineering, Yunnan University, Kunming 650091, China)

Abstract

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.

Suggested Citation

  • Bin Wang & Xuejie Zhang & Xiaobing Zhou & Junyi Li, 2020. "A Gated Dilated Convolution with Attention Model for Clinical Cloze-Style Reading Comprehension," IJERPH, MDPI, vol. 17(4), pages 1-11, February.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:4:p:1323-:d:322302
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