Machine learning-based prediction of message responses using a corpus of response templates

An apparatus comprising:

• at least one processing device comprising a processor coupled to a memory;
• the at least one processing device being configured to implement the following steps:
• obtaining at least one user message sent by at least one user;
• applying the at least one user message of the at least one user to at least one machine learning model that predicts one or more response templates, from a corpus of response templates, that are responsive to the at least one user message, wherein the at least one machine learning model is trained using training data comprising at least one historical user message sent by one or more users and at least one historical agent message sent by at least one customer service agent, wherein the at least one historical agent message:
• (i) has a similarity score with respect to at least one of the response templates, from the corpus of response templates, that satisfies one or more similarity criteria, (ii) is sent in response to the at least one historical user message sent by the one or more users;
• and (iii) is based at least in part on one or more of the at least one response template from the corpus of response templates, wherein an identifier of the at least one response template is used as a classification label in a supervised learning of the at least one machine learning model and wherein the at least one response template and one or more respective variations, that satisfy the one or more similarity criteria, of the at least one response template are considered together during the training of the at least one machine learning model;
• and
• automatically generating at least one response to the at least one user message using the predicted one or more response templates.

A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps:

• obtaining at least one user message sent by at least one user;
• applying the at least one user message of the at least one user to at least one machine learning model that predicts one or more response templates, from a corpus of response templates, that are responsive to the at least one user message, wherein the at least one machine learning model is trained using training data comprising at least one historical user message sent by one or more users and at least one historical agent message sent by at least one customer service agent, wherein the at least one historical agent message:
• (i) has a similarity score with respect to at least one of the response templates, from the corpus of response templates, that satisfies one or more similarity criteria, (ii) is sent in response to the at least one historical user message sent by the one or more users;
• and (iii) is based at least in part on one or more of the at least one response template from the corpus of response templates, wherein an identifier of the at least one response template is used as a classification label in a supervised learning of the at least one machine learning model and wherein the at least one response template and one or more respective variations, that satisfy the one or more similarity criteria, of the at least one response template are considered together during the training of the at least one machine learning model;
• and
• automatically generating at least one response to the at least one user message using the predicted one or more response templates.