Leveraging a New Spanish Corpus for Multilingual and Cross-lingual Metaphor Detection

Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL), pages 228 - 240 December 7-8, 2022 ©2022 Association for Computational Linguistics Leveraging a New Spanish Corpus for Multilingual and Crosslingual Metaphor Detection Elisa Sanchez-Bayona and Rodrigo Agerri HiTZ Center - Ixa, University of the Basque Country UPV/EHU elisa.sanchez@ehu.eus, rodrigo.agerri@ehu.eus Abstract The lack of wide coverage datasets anno- tated 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 an- notated 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 systemati- cally annotate metaphor on real data. We use our newly created dataset to provide competi- tive baselines by fine-tuning several multilin- gual and monolingual state-of-the-art large lan- guage 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 per- form a detailed error analysis that explores the seemingly high transfer of everyday metaphor across these two languages and datasets. 1 Introduction Metaphor can broadly be defined as the interpre- tation of a concept belonging to one domain in terms of another concept from a different domain (Lakoff and Johnson, 1980). Metaphorical expres- sions are recurrent in natural language as a mech- anism to convey abstract ideas through specific experiences related to the real, physical world or to send a stronger message in a discourse. There is a large body of work from various fields such as linguistics, psychology or philosophy that tried to provide a theoretical characterization of metaphor. Some approaches are based on the semantic similar- ity shared between the domains involved (Gentner, 1983; Kirby, 1997), while others explain metaphor- ical uses of language in terms of violation of se- lectional preferences (Wilks, 1975, 1978). Other perspectives focus on the communicative impact of using a metaphorical expression in contrast to its literal counterpart (Searle, 1979; Black, 1962). Following previous work on metaphor detection in Natural Language Processing (NLP) (Steen et al., 2010; Leong et al., 2018), our approach is based on the Conceptual Metaphor Theory of Lakoff and Johnson (1980). They do not conceive metaphors just as a cognitive-linguistic phenomenon com- monly used in our everyday utterances. Instead, metaphors are understood as a conceptual mapping that typically reshapes an entire abstract domain of experience (target) in terms of a different concrete domain (source). The high frequency of metaphors in everyday language has increased the popularity of research for this type of figurative language in the NLP field. One of the reasons behind is the fact that the automatic processing of metaphors is essential to achieve a successful interaction between …

Content truncated to 500 words