Abstract: A method of leveraging improved conversational bots in a contact center system according to an embodiment includes performing knowledge distillation through machine learning to teach a student artificial intelligence model based on a teacher artificial intelligence model and reduce a size of an initial multilingual vocabulary, wherein the student artificial intelligence model includes fewer machine learning embedding layers than the teacher artificial intelligence model, removing tokens from the initial multilingual vocabulary based on a grouping of languages with linguistic similarities to reduce the size of the initial multilingual vocabulary, parsing user text from a human user into one or more tokens, identifying token indexes associated with the respective one or more tokens in a reduced multilingual vocabulary, determining embedding values associated with the identified token indexes, and generating a multilingual embedding output for the user text indicative of user intent based on the embedding values

Abstract: A method of leveraging a global confidence classifier for information retrieval in contact centers according to an embodiment includes receiving, by a computing system, a user query from a contact center client communication with a knowledge-only bot of the computing system, performing, by the computing system, feature extraction on the user query by converting query words of the user query into a numerical vector representation of the user query, identifying, by the computing system, a subset of documents most likely to be responsive to the user query, and re-ranking, by the computing system, the subset of documents most likely to be responsive to the user query based on a global confidence classifier model.

Abstract: A method of leveraging a global confidence classifier for information retrieval in contact centers according to an embodiment includes receiving, by a computing system, a user query from a contact center client communication with a knowledge-only bot of the computing system, performing, by the computing system, feature extraction on the user query by converting query words of the user query into a numerical vector representation of the user query, identifying, by the computing system, a subset of documents most likely to be responsive to the user query, and re-ranking, by the computing system, the subset of documents most likely to be responsive to the user query based on a global confidence classifier model.