Methods and systems for cascading model architecture for providing information on reply emails

Abstract

Methods and systems for a cascading model architecture for providing information on a reply email. Training sample data can be created using a user's incoming reply emails from external computer devices to a user's computer device. A receptivity neural network model can be trained using the training sample data of the reply emails, and a trained receptivity neural network model can be used to determine a receptivity classification for whether new reply emails are positive reply emails or non-positive reply emails. Sample data of non-positive reply emails can be augmented, and an objection identification neural network model can be trained on the augmented sample data of the non-positive reply emails. A trained objection identification neural network model can be used to determine a objection classification for new non-positive reply emails. Explainability information for a classified reply email can be determined to provide information key words and/or key phrases that were used by the trained receptivity neural network model and the trained objection identification neural network model to classify new positive reply emails and new non-positive reply emails.