@inproceedings{abubakar-etal-2025-hausanlp,
    title = "{H}ausa{NLP} at {S}em{E}val-2025 Task 2: Entity-Aware Fine-tuning vs. Prompt Engineering in Entity-Aware Machine Translation",
    author = "Abubakar, Abdulhamid  and
      Abdulkadir, Hamidatu  and
      Ibrahim, Rabiu  and
      Auwal, Abubakar  and
      Wali, Ahmad  and
      Umar, Amina  and
      Bala, Maryam  and
      Sani, Sani Abdullahi  and
      Ahmad, Ibrahim Said  and
      Muhammad, Shamsuddeen Hassan  and
      Abdulmumin, Idris  and
      Marivate, Vukosi",
    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/corrections-2025-08/2025.semeval-1.120/",
    pages = "885--892",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents our findings for SemEval 2025 Task 2, a shared task on entity-aware machine translation (EA-MT). The goal of this task is to develop translation models that can accurately translate English sentences into target languages, with a particular focus on handling named entities, which often pose challenges for MT systems. The task covers 10 target languages with English as the source. In this paper, we describe the different systems we employed, detail our results, and discuss insights gained from our experiments."
}