@inproceedings{ali-etal-2025-leveraging,
title = "Leveraging Loanword Constraints for Improving Machine Translation in a Low-Resource Multilingual Context",
author = "Ali, Felermino D. M. A. and
Lopes Cardoso, Henrique and
Sousa-Silva, Rui",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1406/",
doi = "10.18653/v1/2025.emnlp-main.1406",
pages = "27643--27657",
ISBN = "979-8-89176-332-6",
abstract = "This research investigates how to improve machine translation systems for low-resource languages by integrating loanword constraints as external linguistic knowledge. Focusing on the Portuguese-Emakhuwa language pair, which exhibits significant lexical borrowing, we address the challenge of effectively adapting loanwords during the translation process. To tackle this, we propose a novel approach that augments source sentences with loanword constraints, explicitly linking source-language loanwords to their target-language equivalents. Then, we perform supervised fine-tuning on multilingual neural machine translation models and multiple Large Language Models of different sizes. Our results demonstrate that incorporating loanword constraints leads to significant improvements in translation quality as well as in handling loanword adaptation correctly in target languages, as measured by different machine translation metrics. This approach offers a promising direction for improving machine translation performance in low-resource settings characterized by frequent lexical borrowing."
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<abstract>This research investigates how to improve machine translation systems for low-resource languages by integrating loanword constraints as external linguistic knowledge. Focusing on the Portuguese-Emakhuwa language pair, which exhibits significant lexical borrowing, we address the challenge of effectively adapting loanwords during the translation process. To tackle this, we propose a novel approach that augments source sentences with loanword constraints, explicitly linking source-language loanwords to their target-language equivalents. Then, we perform supervised fine-tuning on multilingual neural machine translation models and multiple Large Language Models of different sizes. Our results demonstrate that incorporating loanword constraints leads to significant improvements in translation quality as well as in handling loanword adaptation correctly in target languages, as measured by different machine translation metrics. This approach offers a promising direction for improving machine translation performance in low-resource settings characterized by frequent lexical borrowing.</abstract>
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%0 Conference Proceedings
%T Leveraging Loanword Constraints for Improving Machine Translation in a Low-Resource Multilingual Context
%A Ali, Felermino D. M. A.
%A Lopes Cardoso, Henrique
%A Sousa-Silva, Rui
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F ali-etal-2025-leveraging
%X This research investigates how to improve machine translation systems for low-resource languages by integrating loanword constraints as external linguistic knowledge. Focusing on the Portuguese-Emakhuwa language pair, which exhibits significant lexical borrowing, we address the challenge of effectively adapting loanwords during the translation process. To tackle this, we propose a novel approach that augments source sentences with loanword constraints, explicitly linking source-language loanwords to their target-language equivalents. Then, we perform supervised fine-tuning on multilingual neural machine translation models and multiple Large Language Models of different sizes. Our results demonstrate that incorporating loanword constraints leads to significant improvements in translation quality as well as in handling loanword adaptation correctly in target languages, as measured by different machine translation metrics. This approach offers a promising direction for improving machine translation performance in low-resource settings characterized by frequent lexical borrowing.
%R 10.18653/v1/2025.emnlp-main.1406
%U https://aclanthology.org/2025.emnlp-main.1406/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1406
%P 27643-27657
Markdown (Informal)
[Leveraging Loanword Constraints for Improving Machine Translation in a Low-Resource Multilingual Context](https://aclanthology.org/2025.emnlp-main.1406/) (Ali et al., EMNLP 2025)
ACL