Can Human Assistance Improve a Computational Poet

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Carolyn E. Lamb, Daniel G. Brown and Charles L.A. Clarke: Can Human Assistance Improve a Computational Poet? In: Bridges 2015. Pages 37–44



Good computational poetry requires sufficiently interesting poetic phrases to be generated or chosen. Different metrics for determining what makes a sufficiently interesting phrase have rarely been directly compared. We directly compare of a number of metrics—topicality, sentiment, and concrete imagery—by collecting human judgments on each metric for the same data set of human-generated phrases, then having humans judge computationally generated poems chosen to include high-scoring phrases against each other. We find through a quantitative analysis that the output of at least some of these metrics is perceived as better than output using none of these metrics.

Extended Abstract


 author      = {Carolyn E. Lamb, Daniel G. Brown and Charles L.A. Clarke},
 title       = {Can Human Assistance Improve a Computational Poet?},
 pages       = {37--44},
 booktitle   = {Proceedings of Bridges 2015: Mathematics, Music, Art, Architecture, Culture},
 year        = {2015},
 editor      = {Kelly Delp, Craig S. Kaplan, Douglas McKenna and Reza Sarhangi},
 isbn        = {978-1-938664-15-1},
 issn        = {1099-6702},
 publisher   = {Tessellations Publishing},
 address     = {Phoenix, Arizona},
 note        = {Available online at \url{ }},
 url         = { },

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