Research on Named Entity Recognition in Improved transformer with R-Drop structure

• URL: http://arxiv.org/abs/2306.08315v1
• Date: Wed, 14 Jun 2023 07:34:27 GMT
• Title: Research on Named Entity Recognition in Improved transformer with R-Drop structure
• Authors: Weidong Ji, Yousheng Zhang, Guohui Zhou, Xu Wang
• Abstract summary: The XLNet-Transformer-R model is proposed in this paper. The Transformer encoder with relative positional encodings are combined to enhance the model's ability to process long text. To prevent overfitting, the R-Drop structure is used to improve the generalization capability.
• Score: 3.677017987610888
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To enhance the generalization ability of the model and improve the effectiveness of the transformer for named entity recognition tasks, the XLNet-Transformer-R model is proposed in this paper. The XLNet pre-trained model and the Transformer encoder with relative positional encodings are combined to enhance the model's ability to process long text and learn contextual information to improve robustness. To prevent overfitting, the R-Drop structure is used to improve the generalization capability and enhance the accuracy of the model in named entity recognition tasks. The model in this paper performs ablation experiments on the MSRA dataset and comparison experiments with other models on four datasets with excellent performance, demonstrating the strategic effectiveness of the XLNet-Transformer-R model.

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