End-to-end Knowledge Graph-enabled Multi-round Dialogue Model Based on Transformer-Reference-Cited by-同舟云学术

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End-to-end Knowledge Graph-enabled Multi-round Dialogue Model Based on Transformer

Published:2024-05-25 Issue: Volume:3 Page:167-172
ISSN:
Container-title:2024 36th Chinese Control and Decision Conference (CCDC)
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Author:

Zhang Qi¹,Yao Man²,Xing Yang¹,Guo Xi Wang³,Qin Shu Jin⁴,Qi Liang⁵,Cao Jinrui⁶

Affiliation:

1. Shenyang University of Chemical Technology,Shenyang,China,110142

2. He University,Shenyang,China,110163

3. Liaoning Petrochemical University,Fushun,China,113001

4. Shangqiu Normal University,Shangqiu,China,476000

5. Shandong University of Science and Technology,Qingdao,China,266590

6. New Jersey City University,Jersey City,USA

Funder

Natural Science Foundation of Shandong Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10587298/10587321/10587623.pdf?arnumber=10587623

Reference29 articles.

1. Multilayer Perceptron New Method for Selecting the Architecture Based on the Choice of Different Activation Functions

2. Theory of the backpropagation neural network

3. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

4. Text understanding from scratch;Zhang;Computer Science,2015

5. Deep recursive neural networks for compositionality in language;Irsoy;Advances in Neural Information Processing Systems,2014

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