基于多尺度相似度特征的答案选择算法

答案选择的主要任务是对问答系统中问题的候选答案排序,当前主流的方法是基于表示学习方法,通过神经网络对问题和答案进行向量表示,然后根据向量相似度对候选答案排序,该类方法忽略了问题和答案的局部关联性。针对这一问题,提出了一种基于多尺度相似度特征的深度学习模型。该模型采取传统的深度学习模型分别提取问题和答案的特征,然后计算各个尺度下的特征相似度得到问答的相似度矩阵,最后采取三种不同的相似度特征学习模型对相似度矩阵学习得到联合相似度。在公开数据集WebQA上进行实验验证,实验结果表明将相似度特征学习方法引入传统深度学习模型获得了较为明显的提升。

Question answer selection based on multiscale similarity feature

The main task of question answer selection is to sort the candidate answers in the question answering system. The current mainstream methods extract the features for the questions and answers through representation learning under the framework of deep learning, the candidate answers are sorted by the similarity between the feature vector of the given question and the feature vector of the candidate answer. However, the methods ignore the relevance of the question and the answer. To solve this problem, this paper proposes a deep learning method based on multi scale similarity features. Firstly, this method is based on the traditional deep learning model to extract the features of the question and the answer respectively. Secondly, the similarity matrix of the question and answer (Q&A) is obtained by calculating the similarity of the feature at each scale. Finally, three feature learning models are proposed to learn the similarity matrix in order to get joint similarity. This paper evaluates our model on the public data set-WebQA. Experiments show that the results are improved compared with traditional deep learning methods by adding the similarity feature learning method.