  We present a novel approach for adapting text written in standard Finnish to
different dialects. We experiment with character level NMT models both by using
a multi-dialectal and transfer learning approaches. The models are tested with
over 20 different dialects. The results seem to favor transfer learning,
although not strongly over the multi-dialectal approach. We study the influence
dialectal adaptation has on perceived creativity of computer generated poetry.
Our results suggest that the more the dialect deviates from the standard
Finnish, the lower scores people tend to give on an existing evaluation metric.
However, on a word association test, people associate creativity and
originality more with dialect and fluency more with standard Finnish.
