Abstract: Systems and methods are described to address shortcomings in a conventional conversation system via a novel technique utilizing artificial neural networks to train the conversation system whether or not to continue context. In some aspects, an interactive media guidance application determines a type of conversation continuity in a natural language conversation comprising first and second queries. The interactive media guidance application determines a first token in the first query and a second token in the second query. The interactive media guidance application identifies entity data for the first and second tokens. The interactive media guidance application retrieves, from a knowledge graph, graph connections between the entity data for the first and second tokens. The interactive media guidance application applies this data as inputs to an artificial neural network.

Abstract: Methods and systems for identifying a meaning of an ambiguous term in a natural language query. The media guidance application isolates first and second terms from a query received from a user and identifies, in a knowledge graph, first and second pluralities of candidate components associated with the first and second terms. The first and second terms each having multiple candidate components indicates the first and second terms have ambiguous meanings. The media guidance application matches each candidate component of the first and second pluralities of candidate components to form a plurality of pairs and determines strength of association for each pair in the plurality of pairs. The media guidance application filters the plurality of pairs by strength of association for each pair and determines a plurality of possible meanings based on the filtered plurality of pairs. The media guidance application selects a meaning from the plurality of possible meanings.

Abstract: Systems and methods are disclosed herein for training a model to accurately determine whether two phrases are conversationally connected. A media guidance application may detect a first phrase and a second phrase, translate each phrase to a string of word types, append each string to the back of a prior string to create a combined string, determine a degree to which any of the individual strings matches any singleton template, and determine a degree to which the combined string matches any conversational template. Based on the degrees to which the individual and combination strings match the singleton and conversational templates, respectively, strengths of association are correspondingly updated.

Abstract: Systems and methods are provided herein for using a trained knowledge graph to accurately determine whether two phrases are conversationally connected. To this end, three phrases are received. The phrases are translated to strings of word types by determining what type of word each word of each phrase represents, and replacing each word of the first phrase with its respective type. A knowledge graph is then accessed to determine strengths of association between a combinations of each string of word types and any conversational category of a plurality of conversational categories. If any of the strengths of association exceed a threshold, the corresponding strings may be determined to be part of a conversation.