基于语义空间距离的相似问句识别方法研究

前沿相关研究将相似问句识别转化为二元问句匹配识别并取得很大进展。但是在自动问答系统的实际应用场景中存在大量数据，这些方法受限于二元问句匹配识别模式，导致时效性不高。针对这一问题，受人脸识别相关研究的启发，该文提出基于语义空间距离衡量的相似问句识别方法（Semantic Space Distance Method，SSDM）。该方法将相似问句识别作为多分类问题进行训练，通过利用人脸识别任务中Margin Softmax损失函数得到语义编码模型。该语义编码模型能够将相似问句在语义空间中聚合，不相似问句在语义空间中远离。SSDM方法将相似问句识别转化成语义空间中的向量距离计算，突破二元问句匹配的方式，保证了一定的高时效性，并且仍然能够在深层语义层面对相似问句进行识别。该方法在Biendata的ASQD数据集中实验测试，取得了比基线方法更优的性能，验证了SSDM方法的有效性。

A Semantic Distance Measure for Similar Question Identification

To address the low efficeiency limited by the binary classification made in the scenario of question answering system, this paper proposes a similar question identification method based on semantic space distance measure (SSDM), which is inspired by related research on face identification. This method obtains a semantic encoder by similar question multi classification process via the Margin Softmax introduced from face identification community. The semantic encoder can aggregate similar question in the semantic space, and make dissimilar questions to be far away from each other in semantic space. SSDM method transforms similar questions identification into vector distance calculation in semantic space, and breaks the binary question matching and guarantees a certain high efficiency. We test the SSDM method in the ASQD dataset from Biendata, and the experimental results show that the SSDM method is better in performance than the baseline method.