18384586. MULTI-TURN DIALOGUE RESPONSE GENERATION USING ASYMMETRIC ADVERSARIAL MACHINE CLASSIFIERS simplified abstract (Capital One Services, LLC)

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MULTI-TURN DIALOGUE RESPONSE GENERATION USING ASYMMETRIC ADVERSARIAL MACHINE CLASSIFIERS

Organization Name

Capital One Services, LLC

Inventor(s)

Oluwatobi Olabiyi of Arlington VA (US)

Erik T. Mueller of Chevy Chase MD (US)

MULTI-TURN DIALOGUE RESPONSE GENERATION USING ASYMMETRIC ADVERSARIAL MACHINE CLASSIFIERS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18384586 titled 'MULTI-TURN DIALOGUE RESPONSE GENERATION USING ASYMMETRIC ADVERSARIAL MACHINE CLASSIFIERS

Simplified Explanation

In a variety of embodiments, machine classifiers may model multi-turn dialogue as a one-to-many prediction task. The machine classifier may be trained using adversarial bootstrapping between a generator and a discriminator with multi-turn capabilities. The machine classifiers may be trained in both auto-regressive and traditional teacher-forcing modes, with the generator including a hierarchical recurrent encoder-decoder network and the discriminator including a bi-directional recurrent neural network. The discriminator input may include a mixture of ground truth labels, the teacher-forcing outputs of the generator, and/or noise data. This mixture of input data may allow for richer feedback on the autoregressive outputs of the generator. The outputs can be ranked based on the discriminator feedback and a response selected from the ranked outputs.

  • Machine classifiers model multi-turn dialogue as a one-to-many prediction task.
  • Training involves adversarial bootstrapping between a generator and a discriminator.
  • Auto-regressive and traditional teacher-forcing modes are used for training.
  • Generator includes a hierarchical recurrent encoder-decoder network.
  • Discriminator includes a bi-directional recurrent neural network.
  • Discriminator input includes ground truth labels, generator outputs, and noise data.
  • Outputs are ranked based on discriminator feedback for response selection.

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      1. Potential Applications
  • Chatbots for customer service
  • Virtual assistants for natural language interaction
  • Automated messaging systems for communication platforms
      1. Problems Solved
  • Improving the quality of responses in multi-turn dialogue systems
  • Enhancing the conversational abilities of AI systems
  • Providing more engaging and interactive interactions with users
      1. Benefits
  • More accurate and contextually relevant responses
  • Better understanding and interpretation of user input
  • Enhanced user experience in conversational AI applications


Original Abstract Submitted

In a variety of embodiments, machine classifiers may model multi-turn dialogue as a one-to-many prediction task. The machine classifier may be trained using adversarial bootstrapping between a generator and a discriminator with multi-turn capabilities. The machine classifiers may be trained in both auto-regressive and traditional teacher-forcing modes, with the generator including a hierarchical recurrent encoder-decoder network and the discriminator including a bi-directional recurrent neural network. The discriminator input may include a mixture of ground truth labels, the teacher-forcing outputs of the generator, and/or noise data. This mixture of input data may allow for richer feedback on the autoregressive outputs of the generator. The outputs can be ranked based on the discriminator feedback and a response selected from the ranked outputs.

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