Abstract: A method, apparatus and system for training an embedding space for content comprehension and response includes, for each layer of a hierarchical taxonomy having at least two layers including respective words resulting in layers of varying complexity, determining a set of words associated with a layer of the hierarchical taxonomy, determining a question answer pair based on a question generated using at least one word of the set of words and at least one content domain, determining a vector representation for the generated question and for content related to the at least one content domain of the question answer pair, and embedding the question vector representation and the content vector representations into a common embedding space where vector representations that are related, are closer in the embedding space than unrelated embedded vector representations. Requests for content can then be fulfilled using the trained, common embedding space.

Abstract: A method, apparatus and system for comprehension-based question answering using a hierarchical taxonomy include receiving a word-based question, associating the word-based question with a layer of the hierarchical taxonomy, in which the hierarchical taxonomy includes at least two layers, each of the at least two layers including respective words resulting in the at least two layers having varying levels complexity, determining which layer of the at least two layers of the hierarchical taxonomy comprises a layer of complexity one level less than the layer of the hierarchical taxonomy associated with the word-based question, and using a pre-trained language model, answering the word-based question using only words associated with the layer of the at least two layers of the hierarchical taxonomy having the one less level of complexity.

Abstract: A method, apparatus and system for training an embedding space for content comprehension and response includes, for each layer of a hierarchical taxonomy having at least two layers including respective words resulting in layers of varying complexity, determining a set of words associated with a layer of the hierarchical taxonomy, determining a question answer pair based on a question generated using at least one word of the set of words and at least one content domain, determining a vector representation for the generated question and for content related to the at least one content domain of the question answer pair, and embedding the question vector representation and the content vector representations into a common embedding space where vector representations that are related, are closer in the embedding space than unrelated embedded vector representations. Requests for content can then be fulfilled using the trained, common embedding space.