Based on the Gemini Large-scale Model, Enhanced Accuracy in Semantic Similarity Detection With the Ernie Model

Authors

  • Zihang Li

DOI:

https://doi.org/10.62051/dpxb3t71

Keywords:

ERNIE, Gemini large-scale model, semantic similarity detection.

Abstract

The rapid development of deep learning models has been a hallmark of recent years. As the world moves towards greater intelligence, there is an urgent need for advancements in technologies like question-answering systems, chatbots, and search and recommendation engines, especially within the service and e-commerce sectors. At the heart of these advancements lies the task of detecting semantic similarity in natural language processing. Against this backdrop, this paper examines the accuracy of semantic similarity detection across different deep learning models, focusing specifically on the LSTM, Transformer, and ERNIE models under identical hyperparameters and configuration settings. The study reveals a common challenge among these models in achieving effective data generalization on the LCQMC dataset. To address this, the paper introduces an innovative approach by combining the highest-performing ERNIE model with the Gemini large-scale model and employing data augmentation techniques to enhance accuracy. This strategy increased accuracy from 82% with the ERNIE model alone to 85%.

Downloads

Download data is not yet available.

References

Zhou, S.K. Research and Application of a Short Text Semantic Similarity Model Based on Deep Learning (Master's thesis, North University of China),2023.

Ding, Q., Chi, H.Y., Yan, X., Xu, G.Y., & Deng, Z.Y. Calculation of Question Semantic Similarity Based on the Transformer Model. Computer Engineering and Design, 2023,(03), 887-893. doi:10.16208/j.issn1000-7024.2023.03.034.

Wang, C.L., Yang, Y.H., Deng, F., & Lai, H.Y. Review of Text Similarity Calculation Methods. Information Science, 2019,(03), 158-168. doi:10.13833/j.issn.1007-7634.2019.03.026.

Mueller, J., & Thyagarajan, A. Siamese Recurrent Architectures for Learning Sentence Similarity.

Meenakshi, D., & Mohamed Shanavas, A.R. A Novel Shared Input-Based LSTM for Semantic Similarity Prediction. JAIT, 2022,4, 387-392. DOI: https://doi.org/10.12720/jait.13.4.387-392

Zhang, Z., Han, X., Liu, Z., Jiang, X., Sun, M., & Liu, Q. ERNIE: Enhanced Language Representation with Informative Entities. 2019,,arXiv preprint arXiv:1905.07129. DOI: https://doi.org/10.18653/v1/P19-1139

Corley, C.D., & Mihalcea, R. Measuring the Semantic Similarity of Texts. In Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment 2005,pp. 13-18. DOI: https://doi.org/10.3115/1631862.1631865

Downloads

Published

12-08-2024

How to Cite

Li, Z. (2024) “Based on the Gemini Large-scale Model, Enhanced Accuracy in Semantic Similarity Detection With the Ernie Model”, Transactions on Computer Science and Intelligent Systems Research, 5, pp. 817–823. doi:10.62051/dpxb3t71.