An evolutionary algorithm approach to poetry generation
Poetry is a unique artifact of the human language faculty, with its defining feature being a strong unity between content and form. Contrary to the opinion that the automatic generation of poetry is a relatively easy task, we argue that it is in fact an extremely difficult task that requires intelligence, world and linguistic knowledge, and creativity. We propose a model of poetry generation as a state space search problem, where a goal state is a text that satisfies the three properties of meaningfulness, grammaticality, and poeticness. We argue that almost all existing work on poetry generation only properly addresses a subset of these properties. In designing a computational approach for solving this problem, we draw upon the wealth of work in natural language generation (NLG). Although the emphasis of NLG research is on the generation of informative texts, recent work has highlighted the need for more flexible models which can be cast as one end of a spectrum of search sophistication, where the opposing end is the deterministically goal-directed planning of traditional NLG. We propose satisfying the properties of poetry through the application to NLG of evolutionary algorithms (EAs), a wellstudied heuristic
Manurung, Hisar
An evolutionary algorithm approach to poetry generation. Poetry is a unique artifact of the human language faculty, with its defining feature being a strong unity between content and form. Contrary to the opinion that the automatic generation of poetry is a relatively easy task, we argue that it is in fact an extremely difficult task that requires intelligence, world and linguistic knowledge, and creativity. We propose a model of poetry generation as a state space search problem, where a goal state is a text that satisfies the three properties of meaningfulness, grammaticality, and poeticness. We argue that almost all existing work on poetry generation only properly addresses a subset of these properties. In designing a computational approach for solving this problem, we draw upon the wealth of work in natural language generation (NLG). Although the emphasis of NLG research is on the generation of informative texts, recent work has highlighted the need for more flexible models which can be cast as one end of a spectrum of search sophistication, where the opposing end is the deterministically goal-directed planning of traditional NLG. We propose satisfying the properties of poetry through the application to NLG of evolutionary algorithms (EAs), a wellstudied heuristic
evolutionary algorithm poetry generation poetry unique artifact human language_faculty define_feature strong unity content form contrary opinion automatic generation poetry relatively easy task argue fact extremely_difficult task require intelligence world linguistic_knowledge creativity propose_model poetry generation state space search problem goal state text satisfies property meaningfulness grammaticality argue almost exist_work poetry generation properly address subset property design computational problem draw_upon wealth work natural_language generation although emphasis generation informative text recent_work highlight_need flexible model cast end spectrum search sophistication oppose end goal directed plan traditional propose satisfy property poetry application evolutionary algorithm heuristic