Evaluation of Sentence Embedding Models for Natural Language Understanding Problems in Russian

Dmitry Popov, Alexander Pugachev, Polina Svyatokum, Elizaveta Svitanko, Ekaterina Artemova

Abstract: 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.