Computer Science ›› 2021, Vol. 48 ›› Issue (6): 196-201.doi: 10.11896/jsjkx.200700100

• Artificial Intelligence • Previous Articles     Next Articles

Text Matching Fusion Model Combining Multi-granularity Information

LYU Le-bin, LIU Qun, PENG Lu, DENG Wei-bin , WANG Chong-yu   

  1. Chongqing Key Laboratory of Computational Intelligence,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Received:2020-07-15 Revised:2020-08-20 Online:2021-06-15 Published:2021-06-03
  • About author:LYU Le-bin,born in 1994,master.His main research interests include natural language processing and so on.(
    LIU Qun,born in 1969,Ph.D,profes-sor,is a member of China Computer Federation.Her main research interests include data mining,complex network and so on.
  • Supported by:
    National Key Research and Development Program of China(2018YFC0832100, 2018YFC0832102) and Key Program of National Natural Science Foundation of China(61936001).

Abstract: Conventional text matching methods are basically divided into representational text matching models and interaction-based text matching models.Since the representation-based text matching model is easy to lose semantic focus and the interaction-based text matching model ignores global information,a text matching fusion model combining multi-granularity information is proposed in this paper.This model fuses two text matching models through interactive attention and expressing attention,and then uses convolutional neural networks to extract multiple different levels of granularity information presented in the text.Then the local important information and global semantic information can be captured.The experimental results on three different text matching tasks show that the proposed model outperform other optimal models by 5.3%,0.4%,1.5% on the NDCG@5 evaluation index respectively.By extracting multiple granularity information of the text and combining interactive attention and expressed attention,the proposed model can effectively pay attention to the text information of different levels,and solve the problem of losing semantics and ignoring global information during the text matching process in the traditional models.

Key words: Expressive attention, Granular network, Interactive attention, Multi-granularity information, Text matching

CLC Number: 

  • TP391
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