Meta4XNLI: A Crosslingual Parallel Corpus for Metaphor Detection and
  Interpretation
Elisa Sanchez-Bayona Rodrigo Agerri
Metaphors, although occasionally unperceived, are ubiquitous in our everyday
language. Thus, it is crucial for Language Models to be able to grasp the
underlying meaning of this kind of figurative language. In this work, we
present Meta4XNLI, a novel parallel dataset for the tasks of metaphor detection
and interpretation that contains metaphor annotations in both Spanish and
English. We investigate language models' metaphor identification and
understanding abilities through a series of monolingual and cross-lingual
experiments by leveraging our proposed corpus. In order to comprehend how these
non-literal expressions affect models' performance, we look over the results
and perform an error analysis. Additionally, parallel data offers many
potential opportunities to investigate metaphor transferability between these
languages and the impact of translation on the development of multilingual
annotated resources.