  A number of morphology-based word embedding models were introduced in recent
years. However, their evaluation was mostly limited to English, which is known
to be a morphologically simple language. In this paper, we explore whether and
to what extent incorporating morphology into word embeddings improves
performance on downstream NLP tasks, in the case of morphologically rich
Russian language. NLP tasks of our choice are POS tagging, Chunking, and NER --
for Russian language, all can be mostly solved using only morphology without
understanding the semantics of words. Our experiments show that
morphology-based embeddings trained with Skipgram objective do not outperform
existing embedding model -- FastText. Moreover, a more complex, but morphology
unaware model, BERT, allows to achieve significantly greater performance on the
tasks that presumably require understanding of a word's morphology.
