Systems and methods for providing explainability of natural language processing

Abstract

Systems or techniques that facilitate systems and methods for providing explainability of natural language processing are provided. In various embodiments, a system can access a plain text clinical sentence. In various aspects, the system can generate, via execution of a first machine learning model, an assertion status classification label for a word of interest in the plain text clinical sentence. In various instances, the system can extract, from a hidden attention layer of the first machine learning model, word-wise attention scores corresponding to the plain text clinical sentence and render, on an electronic display, both the assertion status classification label and a graphical representation of the word-wise attention scores. In various cases, the system can determine, via execution of a second machine learning model, a reliability score for the assertion status classification label, based on the word-wise attention scores, and can render the reliability score on the electronic display.