MGMSN: Multi-Granularity Matching Model Based on Siamese Neural Network - Wang, Xin; Yang, Huimin (Frontiers in Bioengineering and Biotechnology, 2022)
Aiming at overcoming the shortcomings of the existing text matching algorithms. This paper studies the related technologies of sentence matching and dialogue retrieval, and proposes a Multi-Granularity Matching model based on Siamese Neural Network (MGMSN). This method considers both deep semantic similarity and shallow semantic similarity of input sentences to completely mine similar information between sentences. Moreover, considering the problem of OOV(Out Of Vocabulary) in sentences, this paper considers both word and character granularity in deep semantic similarity to further learn information while alleviating the problem of OOV. Finally, a comparative experiment is carried out in the Chinese dataset LCQMC. The experimental results confirm the effectiveness and generalization ability of this method, and the ablation experiment also shows the importance of each part of the model.