@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://preview.aclanthology.org/ingest-emnlp/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."
}