An Expectation-Realization Model for Metaphor Detection
This addresses metaphor detection for natural language processing, but it is incremental as it builds on existing architectures with specific modules.
The authors tackled metaphor detection by proposing an expectation-realization model that learns patterns to identify metaphorical word uses, achieving competitive or better results than state-of-the-art methods on three datasets, with further accuracy gains from ensembling.
We propose a metaphor detection architecture that is structured around two main modules: an expectation component that estimates representations of literal word expectations given a context, and a realization component that computes representations of actual word meanings in context. The overall architecture is trained to learn expectation-realization (ER) patterns that characterize metaphorical uses of words. When evaluated on three metaphor datasets for within distribution, out of distribution, and novel metaphor generalization, the proposed method is shown to obtain results that are competitive or better than state-of-the art. Further increases in metaphor detection accuracy are obtained through ensembling of ER models.