Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation

Abstract

Literary tropes, from poetry to stories, are at the crux of human imagination and communication. Figurative language, such as a simile, goes beyond plain expressions to give readers new insights and inspirations. We tackle the problem of simile generation. Generating a simile requires proper understanding for effective mapping of properties between two concepts. To this end, we first propose a method to automatically construct a parallel corpus by transforming a large number of similes collected from Reddit to their literal counterpart using structured common sense knowledge. We then fine-tune a pretrained sequence to sequence model, BART (Lewis et al., 2019), on the literal-simile pairs to generate novel similes given a literal sentence. Experiments show that our approach generates 88% novel similes that do not share properties with the training data. Human evaluation on an independent set of literal statements shows that our model generates similes better than two literary experts 37% 1 of the times, and three baseline systems including a recent metaphor generation model 71% 2 of the times when compared pairwise. 3 We also show how replacing literal sentences with similes from our best model in machine generated stories improves evocativeness and leads to better acceptance by human judges. * The research was conducted when the author was at USC/ISI. 1 We average 32.6% and 41.3% for 2 humans. 2 We average 82% ,63% and 68% for three baselines. 3 The simile in the title is generated by our best model. Input: Generating similes effortlessly, output: Generating similes like a Pro.

Olá, Bonjour, Salve! XFORMAL: A Benchmark for Multilingual Formality Style Transfer

“…ST datasets that consist of parallel pairs in different styles include: GYAFC for formality (Rao and Tetreault, 2018), Yelp (Shen et al, 2017) and Amazon Product Reviews for sentiment (He and McAuley, 2016), political slant and gender controlled datasets (Prabhumoye et al, 2018), Expert Style Transfer (Cao et al, 2020), PASTEL for imitating personal (Kang et al, 2019), SIMILE for simile generation (Chakrabarty et al, 2020), and others.…”

Exploring Metaphoric Paraphrase Generation

“…With regard to paraphrasing, Stowe et al (2020) use a metaphor masking process to generate parallel training data in which key metaphoric words are hidden, causing the resulting seq2seq model to generate metaphoric words. Chakrabarty et al (2020) build a simile generation system based on pretrained seq2seq models. Similarly, the MER-MAID system uses a semi-supervised data collection method to generate metaphoric pairs, using them to fine-tune a BART-based seq2seq model (Chakrabarty et al, 2021).…”

HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge

“…To the best of our knowledge, we are the first to work on hyperbole generation. The closest work is that of Chakrabarty et al (2020b), who propose an end-to-end approach for simile generation that also utilizes commonsense knowledge predicted by COMeT (Bosselut et al, 2019). However, they only utilize the PROPERTY relation to replace certain parts of literal sentences.…”

“…pared to the many efforts on other figurative languages such as puns, sarcasms, metaphors and similes (He et al, 2019;Chakrabarty et al, 2020a;Su et al, 2020;Yu and Wan, 2019;Chakrabarty et al, 2020b), the exploration of hyperboles is still in the infancy stage: NLP researchers have just started to look at automatic hyperbole detection (Troiano et al, 2018;Kong et al, 2020). According to Claridge (2010), hyperboles are divided into two categories: those at the word or phrase level and those at the clause or sentence level.…”