About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Multimedia Technology and Enhanced Learning. 4th EAI International Conference, ICMTEL 2022, Virtual Event, April 15-16, 2022, Proceedings

Research Article

Multi-round Dialogue Intention Recognition Method for a Chatbot Baed on Deep Learning

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-18123-8_44,
        author={Junmei Li},
        title={Multi-round Dialogue Intention Recognition Method for a Chatbot Baed on Deep Learning},
        proceedings={Multimedia Technology and Enhanced Learning. 4th EAI International Conference, ICMTEL 2022, Virtual Event, April 15-16, 2022, Proceedings},
        proceedings_a={ICMTEL},
        year={2022},
        month={10},
        keywords={Deep learning Chatbots Multiple rounds of dialogue Dialogue intent Intent recognition},
        doi={10.1007/978-3-031-18123-8_44}
    }
    
  • Junmei Li
    Year: 2022
    Multi-round Dialogue Intention Recognition Method for a Chatbot Baed on Deep Learning
    ICMTEL
    Springer
    DOI: 10.1007/978-3-031-18123-8_44
Junmei Li1,*
  • 1: School of Computer Engineering, Jingchu University of Technology
*Contact email: chenweiliang7895@163.com

Abstract

With the continuous development of human-computer dialogue system, more and more dialogue robot products come into people’s lives. However, when human beings use short sentences and omit words, and in the process of identification often face problems such as more text noise, sparse characteristics, polysemy, backward and backward dialogue information. In order to solve the above problem, a deep learning based chatbot multi-round dialogue intention recognition method, according to the fit of deep learning algorithm and chatbot multi-round dialogue intention recognition model, by transforming the problem into a mathematical model, and obtain the final dialogue intention through the calculation of the model. First, the chat dialogue text was preprocessed, and the BERT model was established based on the processing results, the BERT model fused the deep learning model in the B E R T model, established a joint model, and data vectorized the short text of the human-computer dialogue. Finally, the multi-round dialogue intention identification similarity is calculated through the robot, realizing the dialogue intention recognition, and experiments show that the highest accuracy of the recognition method can reach 0.9912, the highest recall rate can reach 0.9914, and the highest f price is 0.9914, which can prove the superiority of the design method.

Keywords
Deep learning Chatbots Multiple rounds of dialogue Dialogue intent Intent recognition
Published
2022-10-19
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-18123-8_44
Copyright © 2022–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL