Systems and methods for generating dynamic conversational responses through aggregated outputs of machine learning models

Abstract

Methods and systems are described herein for generating dynamic conversational responses. For example, dynamic conversational responses may facilitate an interactive exchange with users. Therefore, the methods and systems used specialized methods to enriched data that may be indicative of a user's intent prior to processing that data through the machine learning model, as well as a specialized architecture for the machine learning models that take advantage of the user interface format. For example, a first machine learning model may be trained using a multi-class cross entropy loss function, and a second machine learning model may be trained using a binary cross entropy loss function. A third output may be determined based on a weighted average of first and second outputs from the first and second machine learning models, and a subset of dynamic conversational responses may be generated based on the determined third output.