  The identification of Figurative Language (FL) features in text is crucial
for various Natural Language Processing (NLP) tasks, where understanding of the
author's intended meaning and its nuances is key for successful communication.
At the same time, the use of a specific blend of various FL forms most
accurately reflects a writer's style, rather than the use of any single
construct, such as just metaphors or irony. Thus, we postulate that FL features
could play an important role in Authorship Attribution (AA) tasks. We believe
that our is the first computational study of AA based on FL use. Accordingly,
we propose a Multi-task Figurative Language Model (MFLM) that learns to detect
multiple FL features in text at once. We demonstrate, through detailed
evaluation across multiple test sets, that the our model tends to perform
equally or outperform specialized binary models in FL detection. Subsequently,
we evaluate the predictive capability of joint FL features towards the AA task
on three datasets, observing improved AA performance through the integration of
MFLM embeddings.
