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A personification is a figure of speech that endows inanimate entities withproperties and actions typically seen as requiring animacy. In this paper, weexplore the task of personification generation. To this end, we proposePINEAPPLE: Personifying INanimate Entities by Acquiring ParallelPersonification data for Learning Enhanced generation. We curate a corpus ofpersonifications called PersonifCorp, together with automatically generatedde-personified literalizations of these personifications. We demonstrate theusefulness of this parallel corpus by training a seq2seq model to personify agiven literal input. Both automatic and human evaluations show that fine-tuningwith PersonifCorp leads to significant gains in personification-relatedqualities such as animacy and interestingness. A detailed qualitative analysisalso highlights key strengths and imperfections of PINEAPPLE over baselines,demonstrating a strong ability to generate diverse and creativepersonifications that enhance the overall appeal of a sentence.

PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation