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| A Semantic Distance Measure for Similar Question Identification |
| SU Yulan, CHEN Xin, HONG Yu, ZHU Mengmeng, ZHANG Min |
| School of Computer Science and Technology, Soochow University,Suzhou, Jiangsu 215006, China |
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Abstract 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 multiclassification 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.
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Received: 22 March 2019
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