Multi-Granularity Hierarchical Attention Fusion Networks for RCQA读书笔记

其他 2019-01-29 01:54:47 阅读次数: 0

发表于ACL2018

阿里在SQuAD数据集首次在EM指标上超过人类平均水平的工作

模型：

glove+ELMo embedding

过Bi-LSTM后拼接ELMo embedding

cross-attention相互表征

原表征和cross表征fuse

门机制控制原表征和fuse表征融合结果

对paragraph加入人工特征后果BiLSTM;双线性self-alignment；fuse；BiLSTM

对问题BiLSTM；线性self-alignment

预测答案位置：双线性softmax

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转载自blog.csdn.net/sjh18813050566/article/details/86667692

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