用多智能体系统让翻译更懂文化，保护濒危语言
Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI Systems
- 设计多智能体框架，分工处理翻译、释义、内容合成与偏见评估
- 相比GPT-4o，生成更贴近语境、富含文化内涵的译文
- 适合关注原住民语言、小语种保护的研究者与实践者
语言是文化认同的核心，但全球化和主流语言的霸权使近3000种语言面临灭绝风险。现有AI翻译模型追求效率，却常忽略文化细节、习语和历史意义，导致语言多样性被边缘化。为此，我们提出一种面向弱势语言社区的文化自适应翻译多智能体框架。系统包含翻译、解释、内容生成和偏见评估等专用智能体，利用CrewAI与LangChain提升上下文保真度，并通过外部验证减少偏差。对比分析显示，该框架在生成上下文丰富、文化嵌入性强的译文方面优于GPT-4o，对原住民、区域性及低资源语言具有重要意义。本研究展示了多智能体AI在推动公平、可持续、文化敏感的自然语言技术中的潜力，契合语言模型服务弱势群体的AI治理、文化NLP与可持续NLP原则。完整实验代码已开源：https://github.com/ciol-researchlab/Context-Aware_Translation_MAS
原文摘要 · Abstract (English)
Language is a cornerstone of cultural identity, yet globalization and the dominance of major languages have placed nearly 3,000 languages at risk of extinction. Existing AI-driven translation models prioritize efficiency but often fail to capture cultural nuances, idiomatic expressions, and historical significance, leading to translations that marginalize linguistic diversity. To address these challenges, we propose a multi-agent AI framework designed for culturally adaptive translation in underserved language communities. Our approach leverages specialized agents for translation, interpretation, content synthesis, and bias evaluation, ensuring that linguistic accuracy and cultural relevance are preserved. Using CrewAI and LangChain, our system enhances contextual fidelity while mitigating biases through external validation. Comparative analysis shows that our framework outperforms GPT-4o, producing contextually rich and culturally embedded translations, a critical advancement for Indigenous, regional, and low-resource languages. This research underscores the potential of multi-agent AI in fostering equitable, sustainable, and culturally sensitive NLP technologies, aligning with the AI Governance, Cultural NLP, and Sustainable NLP pillars of Language Models for Underserved Communities. Our full experimental codebase is publicly available at: https://github.com/ciol-researchlab/Context-Aware_Translation_MAS
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