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        <title>Nut-cracking Sledgehammers: Prioritizing Target Language Data over Bigger Language Models for Cross-Lingual Metaphor Detection</title>
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        <namePart type="given">Jakob</namePart>
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            <title>Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD)</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Breitholtz</namePart>
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            <namePart type="given">Shalom</namePart>
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            <namePart type="given">Simon</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Nut-cracking Sledgehammers: Prioritizing Target Language Data over Bigger Language Models for Cross-Lingual Metaphor Detection
%A Schuster, Jakob
%A Markert, Katja
%Y Breitholtz, Ellen
%Y Lappin, Shalom
%Y Loaiciga, Sharid
%Y Ilinykh, Nikolai
%Y Dobnik, Simon
%S Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD)
%D 2023
%8 September
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F schuster-markert-2023-nut
%X 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.
%U https://aclanthology.org/2023.clasp-1.12
%P 98-106

Markdown (Informal)

[Nut-cracking Sledgehammers: Prioritizing Target Language Data over Bigger Language Models for Cross-Lingual Metaphor Detection](https://aclanthology.org/2023.clasp-1.12) (Schuster & Markert, CLASP 2023)

• Nut-cracking Sledgehammers: Prioritizing Target Language Data over Bigger Language Models for Cross-Lingual Metaphor Detection (Schuster & Markert, CLASP 2023)

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.