There Once Was a Really Bad Poet, It Was Automated but You Didn’t Know It

There Once Was a Really Bad Poet, It Was Automated but You Didn’t Know It Jianyou Wang1, Xiaoxuan Zhang1, Yuren Zhou2, Christopher Suh1, Cynthia Rudin1,2 Duke University {1Computer Science, 2Statistics} Department, United States jw542@duke.edu, zhangxiaoxuanaa@gmail.com yuren.zhou@duke.edu, csuh09@gmail.com, cynthia@cs.duke.edu Abstract Limerick generation exemplifies some of the most difficult challenges faced in poetry generation, as the poems must tell a story in only five lines, with constraints on rhyme, stress, and meter. To address these challenges, we introduce LimGen, a novel and fully automated system for limerick generation that outperforms state-of-the-art neural network- based poetry models, as well as prior rule-based poetry models. LimGen consists of three important pieces: the Adaptive Multi-Templated Constraint algorithm that constrains our search to the space of realistic poems, the Multi-Templated Beam Search algorithm which searches efficiently through the space, and the probabilistic Storyline algorithm that provides coherent storylines related to a user-provided prompt word. The resulting limericks satisfy poetic constraints and have thematically coherent storylines, which are sometimes even funny (when we are lucky). 1 Introduction A limerick is a short and catchy 5-line poem that tells a funny, crude, or ironic story. It has strict structural constraints such as an AABBA rhyming scheme, a 99669 syllable count, and an anapestic meter pattern (Legman, 1988). Writing limericks is a challenging task even for human poets, who have to carefully choose, optimize, and even invent new words to satisfy all of the constraints while incorporating creativity and humor. Prior to this paper, there has not been a suc- cessful attempt at realistic automatic limerick generation. Perhaps this is because the task is challenging: Large-scale neural networks often fail to generate decent limericks because the amount of available human-written limericks to learn from is much smaller than other forms of poetry, and because limericks must follow strict structural, meter, and rhyming constraints. Tra- ditional methods for generating limericks instead hard-code the constraints into a template, so that the constraints are obeyed but the generated poems are all extremely similar (resembling Mad Libs, where one fills words into a single template). In this paper, we introduce a novel system of algorithms for automatic limerick generation, denoted as LimGen. LimGen takes a user-specified prompt word and produces a creative and diverse set of limericks related to the prompt. Table 1 shows some of LimGen’s output. LimGen is a rule-based search method. Its main components are: (1) Adaptive Multi-Templated Constraints (AMTC), which constrain LimGen’s search to a space of realistic limericks, leverag- ing knowledge from limerick sentence structures extracted from human poets; (2) the novel Multi- Templated Beam Search (MTBS), which searches the space in a way that fosters diversity in gen- erated poems; and (3) the probabilistic Storyline algorithm, which provides coherent storylines that are thematically related to the prompt word. LimGen relies on the part-of-speech (POS) limerick templates extracted from a small training set and uses a pre-trained language model to fill words into the templates. We used the 345M ver- sion of pre-trained GPT-2 (Radford et al., 2019), …

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