Ensemble of machine learning models for real-time predictions in expert electronic chats

Abstract

An ensemble of machine learning models used for real-time prediction of text for an electronic chat with an expert user. A global machine learning model, e.g., a transformer model, trained with domain specific knowledge makes a domain specific generalized prediction. Another machine learning model, e.g., an n-gram model, learns the specific style of the expert user as the expert user types to generate more natural, more expert user specific text. If specific words cannot be predicted with a desired probability level, another word level machine learning model, e.g., a word completion model, completes the words as the characters are being typed. The ensemble therefore produces real-time, natural, and accurate text that is provided to the expert user. Continuous feedback of the acceptance/rejection of predictions by the expert is used to fine tune one or more machine learning models of the ensemble in real time.