摘要
面向中文问答匹配任务,提出基于深度学习的问答匹配方法,以解决机器学习模型因人工构造特征而导致的特征不足和准确率偏低的问题。在该方法中,主要有三种不同的模型。首先应用组合式的循环神经网络(RNN)与卷积神经网络(CNN)模型去学习句子中的深层语义特征,并计算特征向量的相似度距离。在此模型的基础上,加入两种不同的注意力机制,根据问题构造答案的特征表示去学习问答对中细致的语义匹配关系。实验结果表明,基于组合式的深度神经网络模型的实验效果要明显优于基于特征构造的机器学习方法,而基于注意力机制的混合模型可以进一步提高匹配准确率,其结果最高在平均倒数排序(MRR)和Top-1 accuray评测指标上分别可以达到80.05%和68.73%。
For Chinese question answer matching tasks, a question answer matching method based on deep learning was proposed to solve the problem of lack of features and low accuracy due to artificial structural feature in machine learning. This method mainly includes 3 different models. The first model is the combination of Recurrent Neural Network （RNN） and Convolutional Neural Network （CNN）, which is used to learn the deep semantic features in the sentence and calculate the similarity distance of feature vectors. Moreover, adding two different attention mechanism into this model, the feature representation of answer was constructed according to the question to learn the detailed semantic matching relation of them. Experimental results show that the combined deep nerual network model is superior to the method of feature construction based on machine learning, and the hybrid model based on attention mechanism can further improve the matching accuracy where the best results can reach 80.05% and 68.73% in the standard evaluation of Mean Reciprocal Rank （MRR） and Top-1 accuracy respectively.
出处
《计算机应用》
CSCD
北大核心
2017年第10期2861-2865,共5页
journal of Computer Applications
基金
中央高校基本科研业务费专项资金资助项目(1600219256)
上海市青年科技启明星计划项目(15QA1403900)
上海市自然科学基金资助项目(17ZR1445900)
霍英东教育基金会高等院校青年教师基金资助项目(142002)~~
关键词
问答匹配
深度学习
循环神经网络
卷积神经网络
注意力机制
机器学习
question answer matching
deep learning
Recurrent Neural Network （RNN）
Convolution Neural Network（CNN）
attention mechanism
machine learning
