Abstract: A computer-implemented method for improving a predictive response of a machine learning-based virtual dialogue agent includes identifying an unfilled dialogue slot associated with an active dialogue between a user and the machine learning-based virtual dialogue agent, obtaining subsequent dialogue input data based on prompting the user for the unfilled dialogue slot, and computing a value of the unfilled dialogue slot based on the obtaining of the subsequent dialog input, wherein computing the value of the unfilled dialogue slot includes computing, via a question-answering machine learning model, a slot answer inference comprising the value of the unfilled dialogue slot based on an input of a machine learning-derived query and machine learning-derived context computed for the unfilled dialogue slot.

Abstract: A system and method for improving a predictive accuracy of a machine learning-based virtual conversational agent that includes computing an antecedent context nexus based on query embeddings computed for a preceding query input by a user, wherein the antecedent context nexus includes a pairing of a categorial parameter and a sub-categorical parameter derived based on the query embeddings of the preceding query; creating search logic parameters based on the categorical parameter and the sub-categorical parameter of the antecedent context nexus; executing a context nexus-informed search of a corpus of structured data using at least the search logic parameters; extracting a response candidate from the corpus of structured data based on the execution of the context nexus-informed search; constructing a response to the preceding query based on the extracted response candidate; and returning, via a user interface, the response to the preceding query.

Abstract: A system and method for algorithmically orchestrating conversational dialogue transitions within an automated conversational system may include extracting a set of slots defined within a plurality of utterances and converting the plurality of utterances to a plurality of skeleton utterances. The method may also include grouping the plurality of skeleton utterances into a plurality of skeleton utterance groups, identifying a plurality of valid slot transition pairs based on an assessment of the plurality of skeleton utterance groups, and deriving a plurality of slot ontology groups based on the plurality of valid slot transition pairs. The system and method may use the plurality of distinct slot ontology groups and the plurality of skeleton utterances to facilitate contextually relevant dialogue transitions in the automated conversational system.