Generating Modern Poetry Automatically in Finnish

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 5999–6004, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 5999 Generating Modern Poetry Automatically in Finnish Mika H¨am¨al¨ainen Department of Digital Humanities University of Helsinki mika.hamalainen@helsinki.fi Khalid Alnajjar Department of Computer Science University of Helsinki khalid.alnajjar@helsinki.fi Abstract We present a novel approach for generating poetry automatically for the morphologically rich Finnish language by using a genetic al- gorithm. The approach improves the state of the art of the previous Finnish poem genera- tors by introducing a higher degree of freedom in terms of structural creativity. Our approach is evaluated and described within the paradigm of computational creativity, where the fitness functions of the genetic algorithm are assimi- lated with the notion of aesthetics. The output is considered to be a poem 81.5% of the time by human evaluators. 1 Introduction Poem generation is a challenging task for cre- ative NLG (natural language generation) requir- ing structural integrity in the form of rhyming and meter, grammatical correctness and figurative ex- pression. Poems are meant to be interpreted and therefore the meaning they convey cannot be fully explained by semantics, but they rather require an exploration into the notion of pragmatics. In this paper, we present a novel approach based on a genetic algorithm for creating poetry in Finnish from the stand point of computational creativity. In addition to solving problems related to poems in general, the morphosyntactically com- plex Finnish sets additional requirements for pro- ducing grammatical output. Computational creativity can be seen as a search for creative artefacts in a conceptual space (cf. Wiggins, 2006). Therefore the use of genetic al- gorithm for a creative task is reasonable as it con- ducts a search and picks out the most suitable can- didates based on its fitness function. An important aspect for creativity is that the system should be able to assess its own creations, a notion called appreciation (Colton, 2008) or aesthetic function (Colton et al., 2011) in the literature. The fitness function of the genetic algorithm serves for this exact purpose, as it can score the output in terms of different aesthetic dimensions. 2 Related work In the past, poetry generation has been stud- ied both from the point of view of computa- tional creativity and natural language generation. Poem generation has been tackled with a vari- ety of different methods such as case-based rea- soning (Gerv´as, 2001), templates (Colton et al., 2012), translation with WFSTs (weighted finite- state transducers) (Greene et al., 2010), text trans- formation via word embeddings (Bay et al., 2017) and conditional variational autoencoders (Li et al., 2018). As the field of poem generation has been broadly discussed by Oliveira (2017), we dedi- cate the rest of this section to describing the exist- ing poetry generation work conducted for Finnish within the computational creativity paradigm. We also discuss some previous approaches using ge- netic algorithms. One of the first …

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