  We explore how well a sequence labeling approach, namely, recurrent neural
network, is suited for the task of resource-poor and POS tagging free word
stress detection in the Russian, Ukranian, Belarusian languages. We present new
datasets, annotated with the word stress, for the three languages and compare
several RNN models trained on three languages and explore possible applications
of the transfer learning for the task. We show that it is possible to train a
model in a cross-lingual setting and that using additional languages improves
the quality of the results.
