Meta4XNLI: A Crosslingual Parallel Corpus for Metaphor Detection and Interpretation

According to (Lakoff and Johnson 1980), we can establish a distinction between conceptual metaphors, cognitive mappings that arise from the association between source and target domains, and linguistic metaphors, the expression of these mappings through language. The pervasiveness of metaphors in our daily speech makes it fundamental for language models to be able to process them accordingly, in order to achieve a satisfactory interaction between users and these tools. In addition, metaphor processing may have implications for other Natural Language Processing (NLP) tasks such as Machine Translation (Mao, Lin, and Guerin 2018; Schäffner 2004; Shutova, Teufel, and Korhonen 2013), political discourse analysis (Charteris-Black 2011; Prabhakaran, Rei, and Shutova 2021; Rodríguez et al. 2023) or hate speech (Lemmens, Markov, and Daelemans 2021), among others. Since in this work we study metaphor occurrence in natural language sentences, we will focus on linguistic metaphors only. The most explored task so far is metaphor detection or identification, approached as a sequence labeling task grounded on different theoretical proposals (Wilks 1975, 1978; Searle 1979; Black 1962). The methodology of most widespread use currently are the MIPVU guidelines (Steen et al. 2010), which rely on the mismatch between the basic and contextual meaning of a potential metaphor. The application of this procedure resulted in the publication of the referential dataset VUAM.