@inproceedings{takahashi-etal-2024-ozemi,
    title = "{OZ}emi at {S}em{E}val-2024 Task 1: A Simplistic Approach to Textual Relatedness Evaluation Using Transformers and Machine Translation",
    author = "Takahashi, Hidetsune  and
      Lu, Xingru  and
      Ishijima, Sean  and
      Seo, Deokgyu  and
      Kim, Yongju  and
      Park, Sehoon  and
      Song, Min  and
      Marante, Kathylene  and
      Iso, Keitaro-luke  and
      Tokura, Hirotaka  and
      Ohman, Emily",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Tayyar Madabushi, Harish  and
      Da San Martino, Giovanni  and
      Rosenthal, Sara  and
      Ros{\'a}, Aiala},
    booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.semeval-1.2",
    pages = "7--12",
    abstract = "In this system paper for SemEval-2024 Task 1 subtask A, we present our approach to evaluating the semantic relatedness of sentence pairs in nine languages. We use a mix of statistical methods combined with fine-tuned BERT transformer models for English and use the same model and machine-translated data for the other languages. This simplistic approach shows consistently reliable scores and achieves above-average rank in all languages.",
}