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

arXiv: https://arxiv.org/abs/2209.07752 · HTML: https://mlsanity.com/paper/2209.07752/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 52 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 23 of 23)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://mlsanity.com/paper/2010.05700/) (2020)
- [Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation](https://mlsanity.com/paper/1911.03842/) (2019)
- [KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning](https://mlsanity.com/paper/2009.12677/) (2020)
- [CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling](https://mlsanity.com/paper/1811.10996/) (2018)
- [NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://mlsanity.com/paper/2112.02721/) (2021)
- [Unsupervised Text Style Transfer using Language Models as Discriminators](https://mlsanity.com/paper/1805.11749/) (2018)
- [MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding](https://mlsanity.com/paper/2103.06779/) (2021)
- [GenAug: Data Augmentation for Finetuning Text Generators](https://mlsanity.com/paper/2010.01794/) (2020)
- [Neural Metaphor Detection in Context](https://mlsanity.com/paper/1808.09653/) (2018)
- [Metaphor Generation with Conceptual Mappings](https://mlsanity.com/paper/2106.01228/) (2021)
- [CASCADE: Contextual Sarcasm Detection in Online Discussion Forums](https://mlsanity.com/paper/1805.06413/) (2018)
- [An Enhanced Knowledge Injection Model for Commonsense Generation](https://mlsanity.com/paper/2012.00366/) (2020)
- [Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange](https://mlsanity.com/paper/1909.00088/) (2019)
- [ALOHA: Artificial Learning of Human Attributes for Dialogue Agents](https://mlsanity.com/paper/1910.08293/) (2019)
- [NAREOR: The Narrative Reordering Problem](https://mlsanity.com/paper/2104.06669/) (2021)
- [Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models](https://mlsanity.com/paper/2109.03892/) (2021)
- [SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation](https://mlsanity.com/paper/2108.06643/) (2021)
- [PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically](https://mlsanity.com/paper/2209.06275/) (2022)

## Built on by (most cited first, 1 of 1)

- [CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models](https://mlsanity.com/paper/2210.04191/) (2022)

## Abstract (arXiv)

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.