A Novel Dataset and Three Ways to Approach Automatic Metaphor Detection in German Religious Online Forums

Abstract

In recent years, automatic metaphor detection has received considerable attention within NLP. However, the largest share of research, including most datasets annotated for metaphor, has concentrated on English and a limited set of genres. Automatic metaphor detection for a genre like religious online communication, which is particularly rich in metaphor, remains understudied, in particular since annotated data for this genre is lacking in the first place. This paper aims to close these gaps by offering a novel dataset of posts from German online forums annotated for metaphor, which opens up new research opportunities for automatic metaphor detection for German. Moreover, we present an in-depth exploration in which we evaluate the suitability of different strategies to overcome the relative lack of training data for this task by comparing cross-lingual and cross-genre transfer strategies with the use of LLM prompting. We find that fine-tuning encoder-only language models outperforms the prompting-based approach, that different architectures based on contextual embeddings indeed exhibit considerable differences in their behavior and that smaller in-genre data may be preferable for certain use cases over fine-tuning on larger datasets from different genres.

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