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Article type: Research Article
Authors: Zhang, Zhe | Zhang, Yiyang | Li, Xiang | Qian, Yurong | Zhang, Tao; *
Affiliations: School of Software, XinJiang University, Urumqi, China
Correspondence: [*] Corresponding author. Tao Zhang, School of Software, XinJiang University, urumqi, 830000, China. E-mail: leozht@126.com.
Abstract: This paper proposes a multi-feature spatial convolutional semantic matching model (BMCSA) based on BERT by enriching different feature spatial information of semantic features. BMCSA employs the BERT model to extract the semantic features of the text, then uses the two-dimensional convolutional network to extract different feature spatial information, and finally combines the Attention mechanism to capture the global feature spatial information. We use two different semantic matching data sets and a text inference data set to verify the effectiveness of the proposed model. Experimental results prove that BMCSA is better than the baseline model.
Keywords: Semantic matching, BERT, CNN, Attention mechanism
DOI: 10.3233/JIFS-212624
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4083-4093, 2022
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