AUTHOR=Wang Xin , Yang Huimin 

TITLE=MGMSN: Multi-Granularity Matching Model Based on Siamese Neural Network

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2022.839586

DOI=10.3389/fbioe.2022.839586

ISSN=2296-4185

ABSTRACT=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.