@inproceedings{laarmann-quante-dipper-2026-harder-finding,
    title = "Harder than Finding the Lost Sheep? Towards Automatically Suggesting Deliberate Metaphor Annotations in {G}erman Sermons",
    author = "Laarmann-Quante, Ronja  and
      Dipper, Stefanie",
    editor = "Alves, Diego  and
      Bizzoni, Yuri  and
      Degaetano-Ortlieb, Stefania  and
      Kazantseva, Anna  and
      Pagel, Janis  and
      Szpakowicz, Stan",
    booktitle = "Proceedings of the 10th Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.latechclfl-1.16/",
    pages = "164--175",
    ISBN = "979-8-89176-373-9",
    abstract = "Automatic metaphor detection so far has largely focused on English data annotated for all kinds of metaphors including ubiquitous conventionalized ones. In this paper, we focus on deliberate metaphors in German sermons, i.e., metaphors that are used with a specific communicative goal. This task is harder because there is less training data available, and deliberate metaphors are very rare. Our goal is to support human annotators with automatically generated suggestions, so we strive above all for high recall. Using multilingual transfer learning based on various metaphor datasets and different transformer models, the highest recall we achieve is .70 (precision .10). Our results suggest that larger context windows beyond the sentence level are not helpful and that adding in-domain data even when annotated with different guidelines and in a different language is beneficial."
}