Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)

Selle kollektsiooni püsiv URIhttps://hdl.handle.net/10062/107190

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Annotating and Classifying Direct Speech in Historical Danish and Norwegian Literary Texts
(University of Tartu Library, 2025-03) Al-Laith, Ali; Conroy, Alexander; Degn, Kirstine Nielsen; Bjerring-Hansen, Jens; Hershcovich, Daniel; Johansson, Richard; Stymne, Sara
Analyzing direct speech in historical literary texts provides insights into character dynamics, narrative style, and discourse patterns. In late 19th century Danish and Norwegian fiction direct speech reflects characters' social and geographical backgrounds. However, inconsistent typographic conventions in Scandinavian literature complicate computational methods for distinguishing direct speech from other narrative elements. To address this, we introduce an annotated dataset from the MeMo corpus, capturing speech markers and tags in Danish and Norwegian novels. We evaluate pre-trained language models for classifying direct speech, with results showing that a Danish Foundation Model (DFM), trained on extensive Danish data, has the highest performance. Finally, we conduct a classifier-assisted quantitative corpus analysis and find a downward trend in the prevalence of speech over time.
Evaluating LLM-Generated Explanations of Metaphors – A Culture-Sensitive Study of Danish
(University of Tartu Library, 2025-03) Pedersen, Bolette S.; Sørensen, Nathalie; Nimb, Sanni; Hansen, Dorte Haltrup; Olsen, Sussi; Al-Laith, Ali; Johansson, Richard; Stymne, Sara
In this study, we examine how well Danish culture-specific metaphors are explained by two of the best performing language models for Danish, namely ChatGPT and Llama. For comparison, the explanations are measured against how well cross- lingual (or ’universal’) metaphors are explained by the models; referring here to metaphors that exist in Danish as well as across cultures and languages and in particular in English. To perform our study, we compile a pilot dataset of 150 Danish metaphors and idioms divided tentatively by culture specificity. We prompt the two models and perform a careful qualitative evaluation of the explanations against a four-graded scale. Our studies show that both models are heavily biased towards English since they have much more success in explaining the metaphors that also exist in English than the culture-specific ones, relying presumably on erroneous transfer from English when dealing with the latter. In particular, the sentiment of the culture-specific metaphors seems to be often ’lost in translation’. We further claim that this strong colouring towards English poses a serious problem in the era of LLMs with regards to developing and maintaining cultural and linguistic diversity in other languages.

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Sirvi Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025) Autor "Al-Laith, Ali" järgi