通过多智能体AI系统实现语境感知翻译以保护文化身份
Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI Systems
AI总结：
针对近3000种濒危语言翻译中的文化缺失问题，提出基于CrewAI与LangChain的多智能体文化自适应翻译框架，其效果优于GPT-4o。
摘要：
语言是文化身份的基石，然而全球化及主要语言的主导地位已使近3000种语言面临灭绝风险。现有AI驱动的翻译模型优先考虑效率，却往往无法捕捉文化细微差别、习语表达和历史意义，导致翻译边缘化语言多样性。为应对这些挑战，我们提出一种多智能体AI框架，专为服务不足语言社区的文化自适应翻译而设计。我们的方法利用专门负责翻译、解释、内容综合和偏见评估的智能体，确保语言准确性和文化相关性得到保留。借助CrewAI和LangChain，我们的系统通过外部验证增强语境保真度，同时减轻偏见。比较分析表明，我们的框架优于GPT-4o，能够生成语境丰富且嵌入文化的翻译，这对原住民语言、地区语言和低资源语言而言是一项关键进步。本研究强调多智能体AI在促进公平、可持续且具有文化敏感性的NLP技术方面的潜力，并与“服务不足社区语言模型”的AI治理、文化NLP和可持续NLP支柱保持一致。我们的完整实验代码库公开发布于：https://github.com/ciol-researchlab/Context-Aware_Translation_MAS
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
