  Automatic poetry generation is novel and interesting application of natural
language processing research. It became more popular during the last few years
due to the rapid development of technology and neural computing power. This
line of research can be applied to the study of linguistics and literature, for
social science experiments, or simply for entertainment. The most effective
known method of artificial poem generation uses recurrent neural networks
(RNN). We also used RNNs to generate poems in the style of Adam Mickiewicz. Our
network was trained on the Sir Thaddeus poem. For data pre-processing, we used
a specialized stemming tool, which is one of the major innovations and
contributions of this work. Our experiment was conducted on the source text,
divided into sub-word units (at a level of resolution close to syllables). This
approach is novel and is not often employed in the published literature. The
subwords units seem to be a natural choice for analysis of the Polish language,
as the language is morphologically rich due to cases, gender forms and a large
vocabulary. Moreover, Sir Thaddeus contains rhymes, so the analysis of
syllables can be meaningful. We verified our model with different settings for
the temperature parameter, which controls the randomness of the generated text.
We also compared our results with similar models trained on the same text but
divided into characters (which is the most common approach alongside the use of
full word units). The differences were tremendous. Our solution generated much
better poems that were able to follow the metre and vocabulary of the source
data text.
