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US 11,907,863 B2Granted
Natural language enrichment using action explanations
AssigneeInternational Business Machines Corporation
InventorsDaniela Rosu, Ruchi Mahindru
Life of this patent
Estimating the term…
Abstract
Embodiments are provided that relate to a computer system, a computer program product, and a computer-implemented method for improving performance of a dialog system employing an automated virtual dialog agent. Embodiments involve utilizing an automated virtual agent to receive a natural language request and generate a corresponding response, automatically identifying and resolving a corresponding knowledge gap between the request and response, and refining the automated virtual agent with the resolved knowledge gap.
Claims25
A computer system comprising:
- a processor operatively coupled to a memory;
- and
- an artificial intelligence (AI) platform in communication with the processor, the AI platform having tools to direct performance of a dialog system, the tools comprising:
- a dialog manager configured to receive natural language (NL) related to interaction with an automated virtual dialog agent, the NL comprising one or more dialog events, each of the dialog events including input and output, the input comprising one or more input instances and the output comprising one or more corresponding output instances or actions;
- an AI manager, operatively coupled to the dialog manager, configured to apply the received one or more dialog events to a learning program, the application including the learning program configured to:
- interpret the one or more input instances and the one or more corresponding output instances or actions, including represent the one or more dialog events in a model;
- identify a knowledge gap associated with one or more of the interpreted instances or actions;
- measure distance between subgraphs of the model with a common node label;
- leverage structure and functionality of one or more corresponding models to compare content and to determine any similarities or differences corresponding to concept relations;
- provide an ontology representation of any similarities or differences corresponding to concept relations;
- and
- dynamically generate one or more alternative knowledge items to bridge the identified knowledge gap;
- and
- a director configured to refine the automated virtual dialog agent in relation to the one or more dialog events, the refinement corresponding to the bridged knowledge gap to improve performance of the dialog system.
11 more elements
A computer program product to support active learning, the computer program product comprising:
- a computer readable storage medium having a program code embodied therewith, the program code executable by a processor to:
- receive natural language (NL) relating to interaction with an automated virtual dialog agent of a dialog system, the NL comprising one or more dialog events, each of the one or more dialog events including input and output, the input comprising one more input instances and the output comprising one or more corresponding output instances or output actions;
- apply the received dialogue events to an artificial intelligence (AI) learning program to:
- interpret the one or more input instances and the one or more corresponding output instances or output actions, including represent the one or more dialog events in a model
- identify a knowledge gap associated with one or more of the interpreted instances or actions;
- measure distance between subgraphs of the model with a common node label;
- leverage structure and functionality of one or more corresponding models to compare content and to determine any similarities or differences corresponding to concept relations;
- provide an ontology representation of any similarities or differences corresponding to concept relations;
- and
- dynamically generate one or more alternative knowledge items to bridge the knowledge gap;
- and
- refine the automated virtual dialog agent in relation to the dialog event corresponding to the bridged knowledge gap to improve performance of the dialog system.
9 more elements
A computer-implemented method directed at performance of a dialog system, the method comprising:
- receiving, by a processor of a computing device, natural language (NL) relating to interaction with an automated virtual dialog agent of a dialog system, the NL comprising one or more dialog events, each of the one or more dialog events including input and output, the input comprising one more input instances and the output comprising one or more corresponding output instances or output actions;
- applying, by the processor, the received one or more dialog events to an artificial intelligence (AI) platform comprising a learning program, the applying comprising:
- interpreting the one or more input instances and the one or more corresponding output instances or output actions, including representing the one or more dialog events in a model;
- identifying a knowledge gap associated with the interpreted one or more input instances and the one or more corresponding output instances or output actions;
- measuring distance between subgraphs of the model with a common node label;
- leveraging structure and functionality of one or more corresponding models to compare content and to determine any similarities or differences corresponding to concept relations;
- providing an ontology representation of any similarities or differences corresponding to concept relations;
- and
- dynamically generating one or more alternative knowledge items to bridge the knowledge gap;
- and
- refining the automated virtual dialog agent in relation to the one or more dialog events corresponding to the bridged knowledge gap to improve performance of the dialog system.
8 more elements
A computer system comprising:
- an artificial intelligence (AI) platform in communication with a processor, the AI platform having tools to direct performance of a dialog system, the tools comprising:
- a dialog manager configured to receive natural language (NL) related to interaction with an automated virtual dialog agent of the dialog system, the NL comprising one or more dialog events, each of the dialog events including input and output, the input comprising one or more input instances and the output comprising one or more corresponding output instances;
- an AI manager, operatively coupled to the dialog manager, configured to apply the received one or more dialog events to a learning program, the learning program configured to:
- represent the one or more dialog events in a model, leverage the model to compare text of question answer pairs, and determine one or more similarities and differences in terms of concept relations, wherein the model represents the one or more dialog events;
- identify a knowledge gap between the one or more input instances and the one or more corresponding output instances;
- measure distance between one or more subgraphs of the model with a common node label, wherein the measured distance corresponds to complexity of the identified knowledge gap;
- leverage structure and functionality of one or more corresponding models to compare content and to determine any similarities or differences corresponding to concept relations;
- provide an ontology representation of any similarities or differences corresponding to concept relations;
- and
- dynamically generate one or more alternative knowledge items to bridge the knowledge gap;
- and
- a director configured to refine the automated virtual dialog agent, the refinement corresponding to the bridged knowledge gap.
9 more elements
Dependencies are inferred from the claim text.