Enhancing sentence semantic matching with isotropic batch normalization and generalized pooling operator

Yingjie Shuai

doi:10.1117/12.3026682

9 February 2024 Enhancing sentence semantic matching with isotropic batch normalization and generalized pooling operator

Yingjie Shuai

Author Affiliations +

Proceedings Volume 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023); 130731O (2024) https://doi.org/10.1117/12.3026682
Event: Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 2023, Changsha, China

Abstract

Sentence semantic matching plays a crucial role in various natural language processing (NLP) tasks such as question answering, information retrieval, and text classification. Prior research has made significant contributions to sentence semantic matching, but there is still room for improvement, especially in handling subtle semantic representations. This paper introduces a new approach to sentence semantic matching that integrates Isotropic Batch Normalization and Generalized Pooling Operator, two advanced neural network architectures. By combining these techniques, we aim to enhance the accuracy and efficiency of semantic matching and address the challenges of capturing subtle semantic representations. We compare our approach to existing state-of-the-art models and demonstrate its effectiveness through comprehensive experiments on benchmark datasets.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yingjie Shuai "Enhancing sentence semantic matching with isotropic batch normalization and generalized pooling operator", Proc. SPIE 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 130731O (9 February 2024); https://doi.org/10.1117/12.3026682

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