Ensemble of language models for improved user support

A processing system, comprising:

• a memory comprising computer-executable instructions;
• and a processor configured to execute the computer-executable instructions and cause the processing system to:
• train through unsupervised learning, with a first training dataset comprising unlabeled data, a topic machine learning model to, group, using natural-language processing techniques, the unlabeled data of the first training dataset based on a common theme, each group associated with a topic, whereby the topic machine learning model is trained to parse a customer verbatim and output an indication of the topic of the customer verbatim based on association of the customer verbatim with the common theme;
• train through supervised learning, with a second training dataset comprising sets of sentiment labels, each set of the sets of sentiment labels comprising a labeled customer verbatim and a desired determined sentiment, a sentiment machine learning model to classify, using natural language processing, the customer verbatim and output a indication of a sentiment of a customer of the customer verbatim, wherein the sentiment indicates a customer's satisfaction based on the customer verbatim, and the sentiment machine learning model is trained to reach a minimum of a loss of a loss function by adjusting one or more weights of the sentiment machine learning model to reduce the loss;
• train through supervised learning, with a third training dataset comprising sets of actionability labels, each set of the sets of actionability labels comprising a labeled customer verbatim and a desired actionability score, an actionability machine learning model to classify, using natural language processing, the customer verbatim and output an actionability score for the customer verbatim, wherein the actionability score indicates an estimated resolvability of the customer verbatim by a support service;
• and
• train with reinforcement learning, with a fourth training dataset, a recommendation model to process the output of the topic machine learning model, the output of the sentiment machine learning model, and the output of the actionability machine learning model, and output a recommended response based on the topic, the sentiment, and the actionability score, and the recommendation model is instantiated with a decision matrix capturing prior knowledge.