@inproceedings{shi-etal-2025-ualberta,
title = "{UA}lberta at {S}em{E}val-2025 Task 2: Prompting and Ensembling for Entity-Aware Translation",
author = "Shi, Ning and
Basil, David and
Hauer, Bradley and
Nawal, Noshin and
Riley, Jai and
Teodorescu, Daniela and
Zhang, John and
Kondrak, Grzegorz",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.224/",
pages = "1709--1717",
ISBN = "979-8-89176-273-2",
abstract = "We describe the methods used by our UAlberta team for the SemEval-2025 Task 2 on Entity-Aware Machine Translation (EA-MT). Our methods leverage large language models with prompt engineering strategies suited to this task, including retrieval augmented generation and in-context learning. Our best results overall are obtained with ensembles of multiple models, leveraging named entity knowledge in the dataset. Finally, we provide proof-of-concept experiments showing that lexico-semantic knowledge can be used to identify high-quality translations. We further demonstrate that our methods can function even without gold named entity translations, by using an alternative knowledge base such as BabelNet."
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<abstract>We describe the methods used by our UAlberta team for the SemEval-2025 Task 2 on Entity-Aware Machine Translation (EA-MT). Our methods leverage large language models with prompt engineering strategies suited to this task, including retrieval augmented generation and in-context learning. Our best results overall are obtained with ensembles of multiple models, leveraging named entity knowledge in the dataset. Finally, we provide proof-of-concept experiments showing that lexico-semantic knowledge can be used to identify high-quality translations. We further demonstrate that our methods can function even without gold named entity translations, by using an alternative knowledge base such as BabelNet.</abstract>
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%0 Conference Proceedings
%T UAlberta at SemEval-2025 Task 2: Prompting and Ensembling for Entity-Aware Translation
%A Shi, Ning
%A Basil, David
%A Hauer, Bradley
%A Nawal, Noshin
%A Riley, Jai
%A Teodorescu, Daniela
%A Zhang, John
%A Kondrak, Grzegorz
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F shi-etal-2025-ualberta
%X We describe the methods used by our UAlberta team for the SemEval-2025 Task 2 on Entity-Aware Machine Translation (EA-MT). Our methods leverage large language models with prompt engineering strategies suited to this task, including retrieval augmented generation and in-context learning. Our best results overall are obtained with ensembles of multiple models, leveraging named entity knowledge in the dataset. Finally, we provide proof-of-concept experiments showing that lexico-semantic knowledge can be used to identify high-quality translations. We further demonstrate that our methods can function even without gold named entity translations, by using an alternative knowledge base such as BabelNet.
%U https://aclanthology.org/2025.semeval-1.224/
%P 1709-1717
Markdown (Informal)
[UAlberta at SemEval-2025 Task 2: Prompting and Ensembling for Entity-Aware Translation](https://aclanthology.org/2025.semeval-1.224/) (Shi et al., SemEval 2025)
ACL
- Ning Shi, David Basil, Bradley Hauer, Noshin Nawal, Jai Riley, Daniela Teodorescu, John Zhang, and Grzegorz Kondrak. 2025. UAlberta at SemEval-2025 Task 2: Prompting and Ensembling for Entity-Aware Translation. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 1709–1717, Vienna, Austria. Association for Computational Linguistics.