Generating Metrically Accurate Homeric Poetry with

Recurrent Neural Networks

We investigate the generation of metrically accurate Homeric

poetry using recurrent neural networks (RNN). We assess two models:

a basic encoder-decoder RNN and the hierarchical recurrent encoderdecoder model (HRED). We assess the quality of the generated lines of

poetry using quantitative metrical analysis and expert evaluation. This

evaluation reveals that while the basic encoder-decoder is able to capture

complex poetic meter, it under performs in terms of semantic coherence.

The HRED model, however, produces more semantically coherent lines

of poetry but is unable to capture the meter. Our research highlights the

importance of expert evaluation and suggests that future research should

focus on encoder-decoder models that balance various types of input –

both immediate and long-range.