A framework for annotating and modelling intentions behind metaphor use
This work addresses the problem of understanding metaphor intentions for NLP researchers, but it is incremental as it builds on existing metaphor studies without major breakthroughs.
The paper tackles the lack of a comprehensive taxonomy for intentions behind metaphor use in NLP by proposing a novel 9-category taxonomy and releasing the first dataset annotated for such intentions, and it finds that large language models struggle to infer these intentions in zero- and few-shot settings, with performance indicating a significant challenge.
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.