A personification is a figure of speech that endows inanimate entities with
properties and actions typically seen as requiring animacy. In this paper, we
explore the task of personification generation. To this end, we propose
PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel
Personification data for Learning Enhanced generation. We curate a corpus of
personifications called PersonifCorp, together with automatically generated
de-personified literalizations of these personifications. We demonstrate the
usefulness of this parallel corpus by training a seq2seq model to personify a
given literal input. Both automatic and human evaluations show that fine-tuning
with PersonifCorp leads to significant gains in personification-related
qualities such as animacy and interestingness. A detailed qualitative analysis
also highlights key strengths and imperfections of PINEAPPLE over baselines,
demonstrating a strong ability to generate diverse and creative
personifications that enhance the overall appeal of a sentence.Comment: Accepted to COLING 2022; official Github repo at
https://github.com/sedrickkeh/PINEAPPL