  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.
