  We investigate the performance of sentence embeddings models on several tasks
for the Russian language. In our comparison, we include such tasks as multiple
choice question answering, next sentence prediction, and paraphrase
identification. We employ FastText embeddings as a baseline and compare it to
ELMo and BERT embeddings. We conduct two series of experiments, using both
unsupervised (i.e., based on similarity measure only) and supervised approaches
for the tasks. Finally, we present datasets for multiple choice question
answering and next sentence prediction in Russian.
