@inproceedings{conia-etal-2025-semeval,
title = "{S}em{E}val-2025 Task 2: Entity-Aware Machine Translation",
author = "Conia, Simone and
Li, Min and
Navigli, Roberto and
Potdar, Saloni",
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-1.326/",
pages = "2535--2557",
ISBN = "979-8-89176-273-2",
abstract = "Translating text that contains complex or challenging named entities{---}e.g., cultural-specific book and movie titles, location names, proper nouns, food names, etc.{---}remains a difficult task for modern machine translation systems, including the latest large language models. To systematically study and advance progress in this area, we organized Entity-Aware Machine Translation, or EA-MT, a shared task that evaluates how well systems handle entity translation across 10 language pairs. With EA-MT, we introduce XC-Translate, a novel gold benchmark comprising over 50K manually-translated sentences with entity names that can deviate significantly from word-to-word translations in their target languages. This paper describes the creation process of XC-Translate, provides an overview of the approaches explored by our participants, presents the main evaluation findings, and points toward open research directions, such as contextual retrieval methods for low-resource entities and more robust evaluation metrics for entity correctness. We hope that our shared task will inspire further research in entity-aware machine translation and foster the development of more culturally-accurate translation systems."
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<abstract>Translating text that contains complex or challenging named entities—e.g., cultural-specific book and movie titles, location names, proper nouns, food names, etc.—remains a difficult task for modern machine translation systems, including the latest large language models. To systematically study and advance progress in this area, we organized Entity-Aware Machine Translation, or EA-MT, a shared task that evaluates how well systems handle entity translation across 10 language pairs. With EA-MT, we introduce XC-Translate, a novel gold benchmark comprising over 50K manually-translated sentences with entity names that can deviate significantly from word-to-word translations in their target languages. This paper describes the creation process of XC-Translate, provides an overview of the approaches explored by our participants, presents the main evaluation findings, and points toward open research directions, such as contextual retrieval methods for low-resource entities and more robust evaluation metrics for entity correctness. We hope that our shared task will inspire further research in entity-aware machine translation and foster the development of more culturally-accurate translation systems.</abstract>
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%0 Conference Proceedings
%T SemEval-2025 Task 2: Entity-Aware Machine Translation
%A Conia, Simone
%A Li, Min
%A Navigli, Roberto
%A Potdar, Saloni
%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 conia-etal-2025-semeval
%X Translating text that contains complex or challenging named entities—e.g., cultural-specific book and movie titles, location names, proper nouns, food names, etc.—remains a difficult task for modern machine translation systems, including the latest large language models. To systematically study and advance progress in this area, we organized Entity-Aware Machine Translation, or EA-MT, a shared task that evaluates how well systems handle entity translation across 10 language pairs. With EA-MT, we introduce XC-Translate, a novel gold benchmark comprising over 50K manually-translated sentences with entity names that can deviate significantly from word-to-word translations in their target languages. This paper describes the creation process of XC-Translate, provides an overview of the approaches explored by our participants, presents the main evaluation findings, and points toward open research directions, such as contextual retrieval methods for low-resource entities and more robust evaluation metrics for entity correctness. We hope that our shared task will inspire further research in entity-aware machine translation and foster the development of more culturally-accurate translation systems.
%U https://aclanthology.org/2025.semeval-1.326/
%P 2535-2557
Markdown (Informal)
[SemEval-2025 Task 2: Entity-Aware Machine Translation](https://aclanthology.org/2025.semeval-1.326/) (Conia et al., SemEval 2025)
ACL
- Simone Conia, Min Li, Roberto Navigli, and Saloni Potdar. 2025. SemEval-2025 Task 2: Entity-Aware Machine Translation. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 2535–2557, Vienna, Austria. Association for Computational Linguistics.