@inproceedings{wang-etal-2025-temporal,
    title = "Temporal-Aware Soft Prompt Tuning for Automatic Text Dating",
    author = "Wang, Hai  and
      Liang, Yuzhi  and
      Ren, Han",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-Pages-WenzhengZhang-ZhengyanShi-ShuYang/2025.naacl-long.200/",
    doi = "10.18653/v1/2025.naacl-long.200",
    pages = "3975--3987",
    ISBN = "979-8-89176-189-6",
    abstract = "This paper presents Temporal-aware Soft Prompt Tuning (TASPT), a novel approach for automatic text dating. Unlike existing methods, which often overlook the evolution of word meanings in texts spanning long periods, TASPT incorporates the unique characteristics of historical texts. It introduces a temporal-aware text representation that dynamically captures both semantic variance and invariance. This representation is combined with a soft prompt, enabling efficient parameter tuning for automatic text dating. Experiments show that TASPT outperforms all existing methods on two diachronic datasets: the Twenty-Four Histories and the Royal Society Corpus."
}