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      A Deep Fusion Matching Network Semantic Reasoning Model

      , , , , ,
      Applied Sciences
      MDPI AG

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          Abstract

          As the vital technology of natural language understanding, sentence representation reasoning technology mainly focuses on sentence representation methods and reasoning models. Although the performance has been improved, there are still some problems, such as incomplete sentence semantic expression, lack of depth of reasoning model, and lack of interpretability of the reasoning process. Given the reasoning model’s lack of reasoning depth and interpretability, a deep fusion matching network is designed in this paper, which mainly includes a coding layer, matching layer, dependency convolution layer, information aggregation layer, and inference prediction layer. Based on a deep matching network, the matching layer is improved. Furthermore, the heuristic matching algorithm replaces the bidirectional long-short memory neural network to simplify the interactive fusion. As a result, it improves the reasoning depth and reduces the complexity of the model; the dependency convolution layer uses the tree-type convolution network to extract the sentence structure information along with the sentence dependency tree structure, which improves the interpretability of the reasoning process. Finally, the performance of the model is verified on several datasets. The results show that the reasoning effect of the model is better than that of the shallow reasoning model, and the accuracy rate on the SNLI test set reaches 89.0%. At the same time, the semantic correlation analysis results show that the dependency convolution layer is beneficial in improving the interpretability of the reasoning process.

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          Contributors
          Journal
          ASPCC7
          Applied Sciences
          Applied Sciences
          MDPI AG
          2076-3417
          April 2022
          March 27 2022
          : 12
          : 7
          : 3416
          Article
          10.3390/app12073416
          ea3a0bbb-9091-45d5-9377-0b7c5e9d5022
          © 2022

          https://creativecommons.org/licenses/by/4.0/

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