@inproceedings{anik-etal-2025-preserving,
title = "Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent {AI} Systems",
author = "Anik, Mahfuz Ahmed and
Rahman, Abdur and
Wasi, Azmine Toushik and
Ahsan, Md Manjurul",
editor = "Truong, Sang and
Putri, Rifki Afina and
Nguyen, Duc and
Wang, Angelina and
Ho, Daniel and
Oh, Alice and
Koyejo, Sanmi",
booktitle = "Proceedings of the 1st Workshop on Language Models for Underserved Communities (LM4UC 2025)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.lm4uc-1.7/",
doi = "10.18653/v1/2025.lm4uc-1.7",
pages = "51--60",
ISBN = "979-8-89176-242-8",
abstract = "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 avail able at: github.com/ciol-researchlab/Context-Aware{\_}Translation{\_}MAS."
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<abstract>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 avail able at: github.com/ciol-researchlab/Context-Aware_Translation_MAS.</abstract>
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%0 Conference Proceedings
%T Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI Systems
%A Anik, Mahfuz Ahmed
%A Rahman, Abdur
%A Wasi, Azmine Toushik
%A Ahsan, Md Manjurul
%Y Truong, Sang
%Y Putri, Rifki Afina
%Y Nguyen, Duc
%Y Wang, Angelina
%Y Ho, Daniel
%Y Oh, Alice
%Y Koyejo, Sanmi
%S Proceedings of the 1st Workshop on Language Models for Underserved Communities (LM4UC 2025)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-242-8
%F anik-etal-2025-preserving
%X 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 avail able at: github.com/ciol-researchlab/Context-Aware_Translation_MAS.
%R 10.18653/v1/2025.lm4uc-1.7
%U https://aclanthology.org/2025.lm4uc-1.7/
%U https://doi.org/10.18653/v1/2025.lm4uc-1.7
%P 51-60
Markdown (Informal)
[Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI Systems](https://aclanthology.org/2025.lm4uc-1.7/) (Anik et al., LM4UC 2025)
ACL