|dc.description.abstract||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 search method.
MCGONAGALL is our implemented instance of this approach. We use a linguistic representation
based on Lexicalized Tree Adjoining Grammar (LTAG) that we argue is appropriate for
EA-based NLG. Several genetic operators are implemented, ranging from baseline operators
based on LTAG syntactic operations to heuristic semantic goal-directed operators. Two evaluation
functions are implemented: one that measures the isomorphism between a solution’s
stress pattern and a target metre using the edit distance algorithm, and one that measures the
isomorphism between a solution’s propositional semantics and a target semantics using structural
We conducted an empirical study using MCGONAGALL to test the validity of employing EAs
in solving the search problem, and to test whether our evaluation functions adequately capture
the notions of semantic and metrical faithfulness. We conclude that our use of EAs offers
an innovative approach to flexible NLG, as demonstrated by its successful application to the
poetry generation task.||en