ON POSSIBILITIES OF MULTILINGUAL BERT MODEL FOR DETERMINING SEMANTIC SIMILARITIES OF THE NEWS CONTENT

The results of implementation of modern achievements in the field of Natural Language Processing field based on the methods and models of Deep Learning technologies into the HIPSTO’s system management of content (HIPSTO Publishing, AI Technology, Digital Media, Mobile Apps) are discussed and analyzed. In particular, the possibilities and ways of applying the multilingual BERT model to handle the problem of semantic likeness of news content have been investigated. An efficient method is proposed to define the semantic similarities of the multilingual news content in HIPSTO aggregated news feeds on the basis of the sentence embeddings using the first task of the pretrained multilingual BERT model within the HIPSTO system of content management. The results of the research highlight the effectiveness and promise of this technology within the HIPSTO project. Below the data of its first implementation in HIPSTO are substantiated scientifically and experimentally.