Leveraging a New Spanish Corpus for Multilingual and Crosslingual Metaphor Detection

TL;DR

This paper introduces CoMeta, the first large Spanish corpus for metaphor detection, and demonstrates its effectiveness in multilingual and cross-lingual metaphor identification using state-of-the-art language models.

Contribution

It provides the first extensive Spanish metaphor dataset, applies the MIPVU annotation method, and conducts cross-lingual experiments with English data.

Findings

CoMeta enables competitive metaphor detection in Spanish.

Cross-lingual transfer of metaphor detection is highly effective.

Multilingual models perform well across Spanish and English datasets.

Abstract

The lack of wide coverage datasets annotated with everyday metaphorical expressions for languages other than English is striking. This means that most research on supervised metaphor detection has been published only for that language. In order to address this issue, this work presents the first corpus annotated with naturally occurring metaphors in Spanish large enough to develop systems to perform metaphor detection. The presented dataset, CoMeta, includes texts from various domains, namely, news, political discourse, Wikipedia and reviews. In order to label CoMeta, we apply the MIPVU method, the guidelines most commonly used to systematically annotate metaphor on real data. We use our newly created dataset to provide competitive baselines by fine-tuning several multilingual and monolingual state-of-the-art large language models. Furthermore, by leveraging the existing VUAM English data in addition to CoMeta, we present the, to the best of our knowledge, first cross-lingual experiments on supervised metaphor detection. Finally, we perform a detailed error analysis that explores the seemingly high transfer of everyday metaphor across these two languages and datasets.