@inproceedings{berger-etal-2024-applying,
    title = "Applying Transfer Learning to {G}erman Metaphor Prediction",
    author = "Berger, Maria  and
      Kiwitt, Nieke  and
      Reimann, Sebastian",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.123",
    pages = "1383--1392",
    abstract = "This paper presents results in transfer-learning metaphor recognition in German. Starting from an English language corpus annotated for metaphor at the sentence level, and its machine-translation to German, we annotate 1000 sentences of the German part to use it as a Gold standard for two different metaphor prediction setups: i) a sequence labeling set-up (on the token-level), and ii) a classification (based on sentences) setup. We test two transfer leaning approaches: i) a group of transformer models, and ii) a technique that utilizes bilingual embeddings together with an RNN classifier. We find out that the transformer models do moderately in a zero-shot scenario (up to 61{\%} F1 for classification) and the embeddings approaches do not even beat the guessing baseline (36{\%} F1 for classification). We use our Gold data to fine-tune the classification tasks on target-language data achieving up to 90{\%} F1 with both, the multilingual BERT and the bilingual embeddings. We also publish the annotated bilingual corpus.",
}

This paper presents results in transfer-learning metaphor recognition in German. Starting from an English language corpus annotated for metaphor at the sentence level, and its machine-translation to German, we annotate 1000 sentences of the German part to use it as a Gold standard for two different metaphor prediction setups: i) a sequence labeling set-up (on the token-level), and ii) a classification (based on sentences) setup. We test two transfer leaning approaches: i) a group of transformer models, and ii) a technique that utilizes bilingual embeddings together with an RNN classifier. We find out that the transformer models do moderately in a zero-shot scenario (up to 61{\%} F1 for classification) and the embeddings approaches do not even beat the guessing baseline (36{\%} F1 for classification). We use our Gold data to fine-tune the classification tasks on target-language data achieving up to 90{\%} F1 with both, the multilingual BERT and the bilingual embeddings. We also publish the annotated bilingual corpus.
Berger, Maria Kiwitt, Nieke Reimann, Sebastian
Applying Transfer Learning to German Metaphor Prediction