Multi-Figurative Language Generation

Proceedings of the 29th International Conference on Computational Linguistics, pages 5939–5954 October 12–17, 2022. 5939 Multi-Figurative Language Generation Huiyuan Lai and Malvina Nissim Center for Language and Cognition (CLCG) University of Groningen / The Netherlands {h.lai, m.nissim}@rug.nl Abstract Figurative language generation is the task of reformulating a given text in the desired figure of speech while still being faithful to the orig- inal context. We take the first step towards multi-figurative language modelling by pro- viding a benchmark for the automatic gener- ation of five common figurative forms in En- glish. We train mFLAG employing a scheme for multi-figurative language pre-training on top of BART, and a mechanism for injecting the target figurative information into the en- coder; this enables the generation of text with the target figurative form from another figura- tive form without parallel figurative-figurative sentence pairs. Our approach outperforms all strong baselines. We also offer some qualita- tive analysis and reflections on the relationship between the different figures of speech. 1 Introduction Figurative language is commonly used in speak- ing and writing to accomplish a constellation of communicative goals (Roberts and Kreuz, 1994). Figures of speech, such as metaphors, or idiomatic expressions, can make an expression stand out by making it more interesting and captivating, and can evoke stronger emotions than more factual, literal phrases thereby making the text more engaging. Automatic figurative language generation has received growing attention with the progress of neural networks, especially the emergence of large pre-trained models (Raffel et al., 2020; Lewis et al., 2020). We see there are two core values for this task: (i) computational approaches can be em- ployed to provide a better understanding of lin- guistic phenomena and more specifically in this case different figures of speech; (ii) we can explore how much models can handle creativity and devise ways to employ them in the support of creative writing, so as to yield more varied and human-like generated text, including in the context of machine translation (Guerberof-Arenas and Toral, 2022). Forms Sentences Literal Old Mr. Smith has been teaching here for a very long time. Hyperbole Old Mr. Smith has been teaching here since the Stone Age. Literal My niece will babysit for you for a little bit of money. Idiom My niece will babysit for you for pin money. Literal I hate it when they run the same commercial twice in a row. Sarcasm I love when they run the same commercial twice in a row. Literal He remembers a road of my broken works. Metaphor He made a road of my broken works. Literal You can publish the whole thing old. Simile You can publish the whole thing like a diary. Table 1: Examples of figurative language generation from literal texts. There are many related tasks that have been proposed and studied by NLP researchers, in- cluding the generation of hyperbole (Tian et al., 2021; Zhang and Wan, 2022), idiom (Zhou et al., 2021), sarcasm (Zhu et al., 2019; Chakrabarty et al., 2020a), metaphor (Abe et …

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