@inproceedings{niwa-etal-2021-predicting,
    title = "Predicting Antonyms in Context using {BERT}",
    author = "Niwa, Ayana  and
      Nishiguchi, Keisuke  and
      Okazaki, Naoaki",
    editor = "Belz, Anya  and
      Fan, Angela  and
      Reiter, Ehud  and
      Sripada, Yaji",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Generation",
    month = aug,
    year = "2021",
    address = "Aberdeen, Scotland, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.inlg-1.6/",
    doi = "10.18653/v1/2021.inlg-1.6",
    pages = "48--54",
    abstract = "We address the task of antonym prediction in a context, which is a fill-in-the-blanks problem. This task setting is unique and practical because it requires contrastiveness to the other word and naturalness as a text in filling a blank. We propose methods for fine-tuning pre-trained masked language models (BERT) for context-aware antonym prediction. The experimental results demonstrate that these methods have positive impacts on the prediction of antonyms within a context. Moreover, human evaluation reveals that more than 85{\%} of predictions using the proposed method are acceptable as antonyms."
}