@inproceedings{al-laith-etal-2026-speaking-behalf,
    title = "Speaking on Their Behalf: Detecting Indirect Speech in Historical {D}anish and {N}orwegian Texts",
    author = "Al-Laith, Ali  and
      Conroy, Alexander  and
      Degn, Kirstine  and
      Bjerring-Hansen, Jens  and
      Hershcovich, Daniel",
    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-1.15/",
    pages = "157--163",
    ISBN = "979-8-89176-373-9",
    abstract = "Indirect speech is a fundamental yet understudied form of reported speech that plays a crucial role in literary texts and communication. While direct speech detection has received significant attention in computational linguistics, the automatic identification of indirect speech remains a challenge due to its nuanced linguistic structure and contextual dependencies. This paper focuses on the detection of indirect speech in late 19th-century Scandinavian literature, where its presence has been linked to shifting aesthetic ideals. We present an annotated dataset of 150 segments, each randomly selected from 150 different novels, designed to capture indirect speech in Danish and Norwegian literature. We evaluate four pre-trained language models for classifying indirect speech, with results showing that a Danish Foundation Model (DFM Large), trained on extensive Danish data, has the highest performance. Finally, we conduct a classifier-assisted quantitative corpus analysis and find that the prevalence of indirect speech exhibits fluctuations over time."
}