TULUN: Transparent and Adaptable Low-resource Machine Translation

• URL: http://arxiv.org/abs/2505.18683v1
• Date: Sat, 24 May 2025 12:58:58 GMT
• Title: TULUN: Transparent and Adaptable Low-resource Machine Translation
• Authors: Raphaël Merx, Hanna Suominen, Lois Hong, Nick Thieberger, Trevor Cohn, Ekaterina Vylomova,
• Abstract summary: Tulun is a versatile solution for terminology-aware translation.<n>Our open-source web-based platform enables users to easily create, edit, and leverage terminology resources.
• Score: 30.705550819100424
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine translation (MT) systems that support low-resource languages often struggle on specialized domains. While researchers have proposed various techniques for domain adaptation, these approaches typically require model fine-tuning, making them impractical for non-technical users and small organizations. To address this gap, we propose Tulun, a versatile solution for terminology-aware translation, combining neural MT with large language model (LLM)-based post-editing guided by existing glossaries and translation memories. Our open-source web-based platform enables users to easily create, edit, and leverage terminology resources, fostering a collaborative human-machine translation process that respects and incorporates domain expertise while increasing MT accuracy. Evaluations show effectiveness in both real-world and benchmark scenarios: on medical and disaster relief translation tasks for Tetun and Bislama, our system achieves improvements of 16.90-22.41 ChrF++ points over baseline MT systems. Across six low-resource languages on the FLORES dataset, Tulun outperforms both standalone MT and LLM approaches, achieving an average improvement of 2.8 ChrF points over NLLB-54B.

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