Abstract
Research on metaphor detection (MD) in a multilingual setup has recently gained momentum. As for many tasks, it is however unclear how the amount of data used to pretrain large language models affects the performance, and whether non-neural models might provide a reasonable alternative, especially for MD in low-resource languages. This paper compares neural and non-neural cross-lingual models for English as the source language and Russian, German and Latin as target languages. In a series of experiments we show that the neural cross-lingual adapter architecture MAD-X performs best across target languages. Zero-shot classification with mBERT achieves decent results above the majority baseline, while few-shot classification with mBERT heavily depends on shot-selection, which is inconvenient in a cross-lingual setup where no validation data for the target language exists. The non-neural model, a random forest classifier with conceptual features, is outperformed by the neural models. Overall, we recommend MAD-X for metaphor detection not only in high-resource but also in low-resource scenarios regarding the amounts of pretraining data for mBERT.- Anthology ID:
- 2024.figlang-1.4
- Volume:
- Proceedings of the 4th Workshop on Figurative Language Processing (FigLang 2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico (Hybrid)
- Editors:
- Debanjan Ghosh, Smaranda Muresan, Anna Feldman, Tuhin Chakrabarty, Emmy Liu
- Venues:
- Fig-Lang | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 22–34
- Language:
- URL:
- https://aclanthology.org/2024.figlang-1.4
- DOI:
- 10.18653/v1/2024.figlang-1.4
- Cite (ACL):
- Anna Hülsing and Sabine Schulte Im Walde. 2024. Cross-Lingual Metaphor Detection for Low-Resource Languages. In Proceedings of the 4th Workshop on Figurative Language Processing (FigLang 2024), pages 22–34, Mexico City, Mexico (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Cross-Lingual Metaphor Detection for Low-Resource Languages (Hülsing & Schulte Im Walde, Fig-Lang-WS 2024)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2024.figlang-1.4.pdf