{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T13:32:22Z","timestamp":1674048742869},"reference-count":46,"publisher":"MIT Press - Journals","license":[{"start":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T00:00:00Z","timestamp":1626134400000},"content-version":"vor","delay-in-days":193,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,7,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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).<\/jats:p>","DOI":"10.1162\/tacl_a_00387","type":"journal-article","created":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T19:14:54Z","timestamp":1626376494000},"page":"605-620","update-policy":"http:\/\/dx.doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":1,"title":["There Once Was a Really Bad Poet, It Was Automated but You Didn\u2019t Know It"],"prefix":"10.1162","volume":"9","author":[{"given":"Jianyou","family":"Wang","sequence":"first","affiliation":[{"name":"Duke University, Computer Science Department, United States. jw542@duke.edu"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoxuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Duke University, Computer Science Department, United States. zhangxiaoxuanaa@gmail.com"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuren","family":"Zhou","sequence":"additional","affiliation":[{"name":"Duke University, Statistics Department, United States. yuren.zhou@duke.edu"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher","family":"Suh","sequence":"additional","affiliation":[{"name":"Duke University, Computer Science Department, United States. csuh09@gmail.com"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cynthia","family":"Rudin","sequence":"additional","affiliation":[{"name":"Duke University, Computer Science Department, United States"},{"name":"Duke University, Statistics Department, United States. 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