17804143. Domain-Agnostic Natural Language Processing Using Explainable Interpretation Feedback Models simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

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Domain-Agnostic Natural Language Processing Using Explainable Interpretation Feedback Models

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

YAZAN Obeidi of Markham (CA)

Jaydeep Sen of Bangalore (IN)

Tarun Tater of Mundwa (IN)

Vatche Isahagian of Belmont MA (US)

Vinod Muthusamy of Austin TX (US)

Domain-Agnostic Natural Language Processing Using Explainable Interpretation Feedback Models - A simplified explanation of the abstract

This abstract first appeared for US patent application 17804143 titled 'Domain-Agnostic Natural Language Processing Using Explainable Interpretation Feedback Models

Simplified Explanation

The abstract describes a natural language processing system that can process queries and provide explainable interpretations. Here are the key points:

  • The system receives a natural language query.
  • It automatically detects if the query has implicit intent.
  • If implicit intent is detected, the system generates a modified query with a default inference from a fact sheet.
  • The modified query is presented to the user, who is asked for feedback.
  • If the modified query is approved, a final output is generated.
  • If the modified query is rejected, the system generates an alternative inference and presents a further modified query to the user.

Potential applications of this technology:

  • Customer service chatbots that can understand and interpret user queries.
  • Virtual assistants that can provide explanations for their responses.
  • Information retrieval systems that can generate more accurate queries based on user feedback.

Problems solved by this technology:

  • Ambiguity in natural language queries can be resolved by generating default inferences.
  • Users can provide feedback to improve the accuracy of the system's interpretations.
  • The system can provide explainable outputs, increasing user trust and understanding.

Benefits of this technology:

  • Improved accuracy in understanding and interpreting natural language queries.
  • User feedback helps in refining the system's interpretations.
  • Explainable outputs enhance user trust and satisfaction.


Original Abstract Submitted

An embodiment including a domain-agnostic natural language processing system for processing natural language queries having an explainable interpretation feedback model is provided. The embodiment may include receiving a natural language query. The embodiment may also include to automatically detecting whether the received natural language query includes implicit intent therein. The embodiment may include, in response to detecting implicit intent in the received natural language query, automatically generating a modified query including a default inference from an interpretation fact sheet. The embodiment may further include automatically presenting the modified query to the user and asking the user for feedback on the modified query. The embodiment may also include automatically generating a final output if the modified query was approved, or automatically determining an alternative inference and presenting a further modified query including the alternative inference to the user if the modified query was rejected.

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