@inproceedings{schuster-markert-2023-nut,
    title = "Nut-cracking Sledgehammers: Prioritizing Target Language Data over Bigger Language Models for Cross-Lingual Metaphor Detection",
    author = "Schuster, Jakob  and
      Markert, Katja",
    editor = "Breitholtz, Ellen  and
      Lappin, Shalom  and
      Loaiciga, Sharid  and
      Ilinykh, Nikolai  and
      Dobnik, Simon",
    booktitle = "Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD)",
    month = sep,
    year = "2023",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.clasp-1.12/",
    pages = "98--106",
    abstract = "In this work, we investigate cross-lingual methods for metaphor detection of adjective-noun phrases in three languages (English, German and Polish). We explore the potential of minimalistic neural networks supported by static embeddings as a light-weight alternative for large transformer-based language models. We measure performance in zero-shot experiments without access to annotated target language data and aim to find low-resource improvements for them by mainly focusing on a k-shot paradigm. Even by incorporating a small number of phrases from the target language, the gap in accuracy between our small networks and large transformer architectures can be bridged. Lastly, we suggest that the k-shot paradigm can even be applied to models using machine translation of training data."
}