@inproceedings{miani-etal-2025-cross,
    title = "Cross-Genre Learning for {O}ld {E}nglish Poetry {POS} Tagging",
    author = "Miani, Irene  and
      Stymne, Sara  and
      Darwin, Gregory R.",
    editor = "Zhao, Jin  and
      Wang, Mingyang  and
      Liu, Zhu",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-srw.46/",
    pages = "708--724",
    ISBN = "979-8-89176-254-1",
    abstract = "Poetry has always distinguished itself from other literary genres in many ways, including grammatically and syntactically. These differences are evident not only in modern literature but also in earlier stages. Linguistic analysis tools struggle to address these differences. This paper focuses on the dichotomy between Old English poetry and prose, specifically in the context of the POS tagging task.Two annotated corpora representing each genre were analyzed to show that there are several types of structural differences between Old English poetry and prose. For POS tagging, we conduct experiments on both a detailed tag set with over 200 tags and a mapping to the UPOS tag set with 17 tags. We establish a baseline and conduct two cross-genre experiments to investigate the effect of different proportions of prose and poetry data. Across both tag sets, our results indicate that if the divergence between two genres is substantial, simply increasing the quantity of training data from the support genre does not necessarily improve prediction accuracy. However, incorporating even a small amount of target data can lead to better performance compared to excluding it entirely. This study not only highlights the linguistic differences between Old English poetry and prose but also emphasizes the importance of developing effective NLP tools for underrepresented historical languages across all genres."
}