Traditionally, the Central Bank of Russia used regular expressions for the payment classification as part of its supervisory activities. Regular expressions often spanned multiple pages to cover varied relevant keywords and their forms. We compare this approach to two modern short text classification approaches: fastText and BERT-based transformer in terms of speed, accuracy and flexibility, including few-shot learning.
