A framework for annotating and modelling intentions behind metaphor use

TL;DR

This paper introduces a new taxonomy of intentions behind metaphor use, creates an annotated dataset, and evaluates large language models' ability to infer these intentions, highlighting ongoing challenges.

Contribution

It presents the first comprehensive taxonomy of metaphor intentions, an annotated dataset, and an evaluation of LLMs' inference capabilities in this context.

Findings

LLMs struggle to accurately infer metaphor intentions.

The dataset enables better analysis of metaphor use in NLP.

Metaphor intention inference remains a challenging task for current models.

Abstract

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.