DATA PROCESSING SYSTEMS FACILITATING NATURAL LANGUAGE PROCESSING FOR CONVERSATIONAL DATA

Abstract

Systems and methods iteratively train, using training data, a natural language processing (NLP) model to interpret conversational input during a conversation between an agent and a user by predicting key statements to be used in the prediction of a user intent, the training comparing outputs to a target variable during each iteration and adjusting parameters of the NLP model during each iteration to improve predictability of the user intent from the conversational input. Real-time conversational data is transmitted to the NLP model and the trained NLP algorithm derives key statements predicted to indicate intents and predicts one or more user intents based on the data from the conversation. One or more pre-filled forms predicted to effectuate the one or more user intents is generated, the pre-filled forms including generated text derived from information from the data of the conversation, and the form is transmitted to an agent device.

CPC Classifications