  In this study we address the problem of automated word stress detection in
Russian using character level models and no part-speech-taggers. We use a
simple bidirectional RNN with LSTM nodes and achieve the accuracy of 90% or
higher. We experiment with two training datasets and show that using the data
from an annotated corpus is much more efficient than using a dictionary, since
it allows us to take into account word frequencies and the morphological
context of the word.
