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.- Anthology ID:
- 2023.clasp-1.12
- Volume:
- Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD)
- Month:
- September
- Year:
- 2023
- Address:
- Gothenburg, Sweden
- Editors:
- Ellen Breitholtz, Shalom Lappin, Sharid Loaiciga, Nikolai Ilinykh, Simon Dobnik
- Venue:
- CLASP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 98–106
- Language:
- URL:
- https://aclanthology.org/2023.clasp-1.12
- DOI:
- Cite (ACL):
- Jakob Schuster and Katja Markert. 2023. Nut-cracking Sledgehammers: Prioritizing Target Language Data over Bigger Language Models for Cross-Lingual Metaphor Detection. In Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD), pages 98–106, Gothenburg, Sweden. Association for Computational Linguistics.
- Cite (Informal):
- Nut-cracking Sledgehammers: Prioritizing Target Language Data over Bigger Language Models for Cross-Lingual Metaphor Detection (Schuster & Markert, CLASP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.clasp-1.12.pdf