@inproceedings{gundam-etal-2025-zero-semeval,
    title = "Zero at {S}em{E}val-2025 Task 2: Entity-Aware Machine Translation: Fine-Tuning {NLLB} for Improved Named Entity Translation",
    author = "Gundam, Revanth  and
      Marri, Abhinav  and
      Malladi, Advaith  and
      Mamidi, Radhika",
    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/transition-to-people-yaml/2025.semeval-1.157/",
    pages = "1187--1191",
    ISBN = "979-8-89176-273-2",
    abstract = "Machine Translation (MT) is an essential tool for communication amongst people across different cultures, yet Named Entity (NE) translation remains a major challenge due to its rarity in occurrence and ambiguity. Traditional approaches, like using lexicons or parallel corpora, often fail to generalize to unseen entities, and hence do not perform well. To address this, we create a silver dataset using the Google Translate API and fine-tune the facebook/nllb200-distilled-600M model with LoRA (LowRank Adaptation) to enhance translation accuracy while also maintaining efficient memory use. Evaluated with metrics such as BLEU, COMET, and M-ETA, our results show that fine-tuning a specialized MT model improves NE translation without having to rely on largescale general-purpose models."
}