  Metaphors are part of everyday language and shape the way in which we
conceptualize the world. Moreover, they play a multifaceted role in
communication, making their understanding and generation a challenging task for
language models (LMs). While there has been extensive work in the literature
linking metaphor to the fulfilment of individual intentions, no comprehensive
taxonomy of such intentions, suitable for natural language processing (NLP)
applications, is available to present day. In this paper, we propose a novel
taxonomy of intentions commonly attributed to metaphor, which comprises 9
categories. We also release the first dataset annotated for intentions behind
metaphor use. Finally, we use this dataset to test the capability of large
language models (LLMs) in inferring the intentions behind metaphor use, in
zero- and in-context few-shot settings. Our experiments show that this is still
a challenge for LLMs.
