Journal of Applied Science and Engineering

Published by Tamkang University Press


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Yongqiang Wang1This email address is being protected from spambots. You need JavaScript enabled to view it., Li Yang1, and Zhixin Lun2

1College of Artificial Intelligence, Tangshan University, Tangshan, 063000, China

2Computing Center, Tangshan University, Tangshan, 063000, China


Received: June 20, 2023
Accepted: November 2, 2023
Publication Date: March 1, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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In the context of information big data, breakthroughs have been made in artificial intelligence, natural machine language and other technologies, and robot interactive dialogue has become a reality. Seq2seq is a common natural machine language processing technology, but the traditional Seq2seq dialogue model faces the problem of lack of semantic information and long-distance dependence. Therefore, the traditional Seq2seq technology is studied, and BiLSTM and attention mechanism are used to optimize the Seq2seq dialogue model. The simulation experiment test shows that in the iterative loss performance test of the dialogue model with an increased attention mechanism, the overall curve of the BiLSTM model has a gentle trend, and the loss is lower than that of the LSTM model. At 100 iterations, the loss value of the LSTM model is 0.36 and the loss value of the BiLSTM model is 0.17 . In the music scene dialogue test, the LSTM model could not accurately understand the meaning of the dialogue, and the satisfaction rate was 45%. The BiLSTM model accurately recognized the meaning of the dialogue and responded correctly, with a satisfaction rating of 69%. The innovation of research content adopts BiLSTM and attention mechanism to optimize the traditional Seq2seq dialogue model, improve the language analysis ability of robots, and provide important reference significance for the development of robot intelligence.

Keywords: Data mining; Machine language; BiLSTM model; Seq2seq model

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