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Generating metaphors is a challenging task as it requires a properunderstanding of abstract concepts, making connections between unrelatedconcepts, and deviating from the literal meaning. In this paper, we aim togenerate a metaphoric sentence given a literal expression by replacing relevantverbs. Based on a theoretically-grounded connection between metaphors andsymbols, we propose a method to automatically construct a parallel corpus bytransforming a large number of metaphorical sentences from the Gutenberg Poetrycorpus (Jacobs, 2018) to their literal counterpart using recent advances inmasked language modeling coupled with commonsense inference. For the generationtask, we incorporate a metaphor discriminator to guide the decoding of asequence to sequence model fine-tuned on our parallel data to generatehigh-quality metaphors. Human evaluation on an independent test set of literalstatements shows that our best model generates metaphors better than threewell-crafted baselines 66% of the time on average. A task-based evaluationshows that human-written poems enhanced with metaphors proposed by our modelare preferred 68% of the time compared to poems without metaphors.

MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding