PINEAPPLE : Personifying IN animate Entities by Acquiring Parallel Personification Data for Learning Enhanced Generation (2022)

Abstract: 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.

## introduction personification is the attribution of animate actions or characteristics to an entity that is inherently inanimate. consider, for example, the sentence "the stars danced playfully in the moonlit sky." here, the vibrance of the stars (something inanimate) is being likened to dancing playfully, which is a distinctly animate action. by allowing readers to construct clearer mental images, personifications enhance the creativity of a piece of text @xcite @xcite . being able to automatically identify and generate personifications is important for multiple reasons. first, humans naturally use personifications when communicating. when we say something like "my phone has died," or "my car is not cooperating," to a dialogue system, it is important that the dialogue system understands the intended meaning behind these personifications. if these systems interpret personifications literally, they may fail in several downstream tasks (e.g. classification) since their understanding is incorrect. being able to generate personifications also allows dialogue agents and language models to be more creative and generate more figurative sentences. personification generation has additional applications such as ai-assisted creative writing, since machine-generated figures of speech have been shown to enhance the interestingness of written text @xcite . despite previous success in generating other figures of speech such as similes @xcite , metaphors @xcite , hyperboles @xcite , irony @xcite , and sarcasm @xcite , personification generation is relatively underexplored. one key challenge is that personifications do not have an explicit syntactic structure unlike similes which use 'like' or 'as'. they are also not as loosely-defined as metaphors. rather, a personification requires identifying an inanimate subject together with actions or descriptions which are commonly used on animate subjects. these steps are challenging and require our models to understand commonsense concepts including animacy. in line with exploring the task of personification generation, we present three main contributions: (1) we curate a dataset, personifcorp, of diverse personification examples from various sources. (2) we propose a method called pineapple to automatically de-personify personifications and create a parallel corpus of personification data along with their literalizations. (3) given our parallel corpus, we train a seq2seq model to personify given text. we conduct automatic and human evaluation and qualitative analysis of the generated outputs. ## datasets we curate a dataset called personifcorp of 511 personifications, with 236 coming from a publicly available open-sourced list foot_0 and 275 manuallyfiltered personifications extracted from the deja dataset @xcite . the deja dataset is an image-captioning dataset containing a "figurative" subset of size 6000, of which 4.1% of the captions are labelled as personifications. we extract these personifications and combine them with our existing list to form the final personifcorp dataset. we also note that although it is possible to further expand this dataset (e.g. by ad hoc searching for miscellaneous sites and examples online), we ultimately decide against this after performing an initial investigation. when we attempted to look for additional examples, we found that many of the new examples we found were near-duplicates of existing personifications already in our list. in addition, ad hoc searching can give at most a few hundred examples, which will lead to very incremental gains in performance. this is impractical if we want to collect a large-scale dataset. we hence decided to restrict ourselves to sentences from reasonably well-vetted, already existing corpora from *cl prior art or officially released data from sources like kaggle/semeval shared tasks. ## related work we present the linguistic underpinnings behind the topic-attribute framework used in this paper and explore how other types of figures of speech are generated. we also explore what makes personification generation so challenging. linguistic motivations. personifications traditionally do not have clearly defined classifications. in fact, even within the linguistic community, the definition of a personification is not always very clear-cut @xcite . a study by @xcite examines the personification structure "nonhuman subject + predicate verb (used for humans only) + others," as well as the structure "others + predicate verb (used for humans only) + nonhuman object + others." we generalize and repackage these concepts, renaming the subject as the topic and the predicate verb as the attribute. in doing so, we are able to capture more general notions of animacy beyond just verbs. ## conclusion and future work in this paper, we explored the task of personification generation. we curated a dataset of personifications and proposed the pineapple method to automatically de-personify text. using our parallel corpus, personifcorp, we trained a seq2seq model (bart) to generate creative personifications. through automatic, human, and qualitative evaluation, we demonstrated that these personifications make sentences more interesting and enhance the text's overall appeal. our finetuned model successfully does this while maintaining a high level of fluency and meaning perservation. some weaknesses of our model include failing to personify more complex sentence structures, and occasionally failing to preserve the exact meaning of the original sentence. we also believe that our model still has room to grow in terms of the diversity of personifications generated. further, we can explore unsupervised style transfer methods @xcite @xcite , where we regard the personificationhood of a sentence as a kind of style. we can also investigate data augmentation methods @xcite @xcite to further expand our dataset. another promising direction would be to explore ways to acquire more control over which parts of the sentence are personified or what types of personifications are generated, or to apply this to make dialogue agents more interesting, e.g. by giving them more personality @xcite .