Coreference Resolution (CR) is one of the most difficult tasks in the field of Natural Language Processing due to the lack of deeply and comprehensively understanding the semantic meaning of the mention in not only the sentence-level context but also the entire document-level context. To the best of our knowledge, the previous proposed models often address the coreference resolution task in two steps: 1) detect all possible mention candidates, 2) score and cluster them into chains. We instead propose a new approach which reforms the coreference resolution task to the task of learning sentence-level coreferential relations. Additionally, by leveraging the power of state-of-the-art language representation models such as BERT, ELMo, it was possible to achieve cutting edge results on Russian datasets.