A3Net:Adversarial-and-Attention Network for Machine Reading Comprehension

摘要：

　　In this paper,we introduce Adversarial-and-attention Network(A3Net)for Machine Reading Comprehension.This model extends existing approaches from two perspectives.First,adversarial training is applied to several target variables within the model,rather than only to the inputs or embeddings.We control the norm of adversarial perturbations according to the norm of original target variables,so that we can jointly add perturbations to several target variables during training.As an effective regularization method,adversarial training improves robustness and generalization of our model.Second,we propose a multi-layer attention network utilizing three kinds of highefficiency attention mechanisms.Multi-layer attention conducts interaction between question and passage within each layer,which contributes to reasonable representation and understanding of the model.Combining these two contributions,we enhance the diversity of dataset and the information extracting ability of the model at the same time.Meanwhile,we construct A3Net for theWebQA dataset.Results show that our model outperforms the state-of-the-art models(improving Fuzzy Score from 73.50%to 77.0%).

关键词： Machine Reading Comprehension Adversarial training Multi-layer attention

作者: Jiuniu Wang Xingyu Fu Guangluan Xu Yirong Wu Ziyan Chen Yang Wei Li Jin

作者单位: Key Laboratory of Technology in Geo-spatial Information Processing and Application System,Institute Key Laboratory of Technology in Geo-spatial Information Processing and Application System,Institute

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 64-75

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）