@inproceedings{lau-wu-2024-cyut,
    title = "{CYUT} at {S}em{E}val-2024 Task 7: A Numerals Augmentation and Feature Enhancement Approach to Numeral Reading Comprehension",
    author = "Lau, Tsz-yeung  and
      Wu, Shih-hung",
    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://preview.aclanthology.org/jlcl-multiple-ingestion/2024.semeval-1.87/",
    doi = "10.18653/v1/2024.semeval-1.87",
    pages = "579--585",
    abstract = "This study explores Task 2 in NumEval-2024, which is SemEval-2024(Semantic Evaluation)Task 7 , focusing on the Reading Comprehension of Numerals in Text (Chinese). The datasetutilized in this study is the Numeral-related Question Answering Dataset (NQuAD), and the model employed is BERT. The data undergoes preprocessing, incorporating Numerals Augmentation and Feature Enhancement to numerical entities before model training. Additionally, fine-tuning will also be applied. The result was an accuracy rate of 77.09{\%}, representing a 7.14{\%} improvement compared to the initial NQuAD processing model, referred to as the Numeracy-Enhanced Model (NEMo)."
}