Applying Transfer Learning to German Metaphor Prediction

Maria Berger, Nieke Kiwitt, Sebastian Reimann

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.
Anthology ID:
2024.lrec-main.123
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
1383–1392
Language:
URL:
https://aclanthology.org/2024.lrec-main.123
DOI:
Bibkey:
Cite (ACL):
Maria Berger, Nieke Kiwitt, and Sebastian Reimann. 2024. Applying Transfer Learning to German Metaphor Prediction. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 1383–1392, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Applying Transfer Learning to German Metaphor Prediction (Berger et al., LREC-COLING 2024)
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https://preview.aclanthology.org/add_acl24_videos/2024.lrec-main.123.pdf
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