The article discusses using RoBERTa and Stacked Bidirectional GRU for accurately recognizing specific named entities in Chinese text. The researchers from Beijing University of Technology aimed to improve the precision of identifying these entities. By combining RoBERTa, a type of language model, with Stacked Bidirectional GRU, a type of neural network, they were able to achieve better results in identifying fine-grained named entities in Chinese text. Their approach showed promising outcomes in accurately recognizing and categorizing different types of named entities in the Chinese language.