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A multi-agent system based on HNC for domain-specific machine translation.

Ming Li1, Keliang Zhang2

  • 1College of International Studies, National University of Defense Technology, Nanjing, 210000, China. limingyaya@qq.com.

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Summary
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This study introduces a novel domain classification system and multi-agent approach to significantly improve domain-specific machine translation quality for large language models (LLMs). The method enhances LLM translation accuracy across various specialized fields.

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Area of Science:

  • Natural Language Processing
  • Machine Translation
  • Artificial Intelligence

Background:

  • Current domain-specific machine translation suffers from low quality due to a lack of effective domain classification theory.
  • Large language models (LLMs) require specialized approaches for accurate domain-specific translation.

Purpose of the Study:

  • To propose a domain classification system and a novel method to enhance domain-specific machine translation.
  • To improve the translation capabilities of LLMs in specialized fields through joint system utilization.

Main Methods:

  • Developed a domain classification system based on Hierarchical Neural Networks (HNC).
  • Designed a multi-agent system for domain-specific machine translation.
  • Implemented a prompt generation method guided by the domain classification system for LLMs.

Main Results:

  • The proposed method successfully enhances the domain-specific machine translation capabilities of LLMs.
  • Positive results were observed in cross-lingual translation for science, technology, health, and culture domains.
  • Effective English-Chinese translation improvements were noted in politics, economy, military, and culture domains.

Conclusions:

  • The integrated approach of domain classification and LLMs significantly boosts domain-specific translation quality.
  • The proposed system offers a practical workflow for enhancing specialized machine translation.
  • This research addresses a critical gap in domain-specific machine translation theory and application.