基于双线性函数注意力Bi-LSTM模型的机器阅读理解

发布时间：2018-04-08 15:14

  本文选题：深度学习　切入点：机器阅读理解　出处：《计算机科学》2017年S1期

【摘要】：近年来,随着深度学习(Deep Learning)在机器阅读理解(Machine Reading Comprehension)领域的广泛应用,机器阅读理解迅速发展。针对机器阅读理解中的语义理解和推理,提出一种双线性函数注意力(Attention)双向长短记忆网络(Bi directional-Long Short-Term Memory)模型,较好地完成了在机器阅读理解中抽取文章、问题、问题候选答案的语义并给出了正确答案的任务。将其应用到四六级(CET-4,CET-6)听力文本上测试,测试结果显示,以单词为单位的按序输入比以句子为单位的按序输入准确率高2%左右;此外,在基本的模型之上加入多层注意力转移的推理结构后准确率提升了8%左右。
[Abstract]:In recent years, with the extensive application of deep learning in machine Reading comprehension, machine reading comprehension has developed rapidly.Aiming at semantic understanding and reasoning in machine reading, a bi-directional long and short memory network (Bi directional-Long Short-Term memory) model with bilinear function attentiveness is proposed, which can be used to extract articles from machine reading comprehension.The semantics of the question candidate answer and the task of giving the correct answer.It was applied to CET-4 / CET-6) listening text test. The results showed that the accuracy of sequential input in word units was about 2% higher than that in sentence units.The accuracy is improved by about 8% after adding the reasoning structure of multi-layer attention shift to the basic model.
【作者单位】： 中国人民解放军理工大学;
【分类号】：TP18;TP391.1
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本文编号：1722162