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Short Text Paraphrase Identification Model Based on RDN-MESIM.

Jing Li1,2, Dezheng Zhang1,2, Aziguli Wulamu1,2

  • 1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Computational Intelligence and Neuroscience
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This summary is machine-generated.

Artificial intelligence (AI) enhances tax consulting by improving data inquiry efficiency. A new RDN-MESIM model achieves 97.63% accuracy in identifying paraphrased tax information.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Tax Consulting

Background:

  • AI technology is rapidly advancing and widely applied in social services.
  • Tax consulting can benefit from AI to optimize data inquiry processes.
  • Current methods may lack efficiency in handling complex tax-related queries.

Purpose of the Study:

  • To propose a novel model for paraphrase identification in tax consulting.
  • To enhance the efficiency and quality of tax data inquiry using AI.
  • To adapt existing models for improved performance in the tax domain.

Main Methods:

  • Designed a novel Recurrent Neural Network-Dense (RDN) network.
  • Modified the original Enhanced Sequential Inference Model (ESIM) to integrate with the RDN structure.
  • Developed a hybrid model named RDN-MESIM for paraphrase identification.

Main Results:

  • The RDN-MESIM model achieved a high accuracy of 97.63%.
  • RDN-MESIM outperformed existing relevant models in paraphrase identification tasks.
  • The proposed model demonstrates significant improvements in tax data inquiry.

Conclusions:

  • The RDN-MESIM model is effective for paraphrase identification in tax consulting.
  • AI-driven solutions can substantially improve tax consultation services.
  • The novel RDN-MESIM architecture offers a promising direction for future research.