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

=MULTI-TURN DIALOGUE RESPONSE GENERATION USING ASYMMETRIC ADVERSARIAL MACHINE CLASSIFIERS=


==Organization Name==

[[:Category:Capital One Services, LLC|Capital One Services, LLC]]


[[Category:Capital One Services, LLC]]

==Inventor(s)==

[[:Category:Oluwatobi Olabiyi of Arlington VA (US)|Oluwatobi Olabiyi of Arlington VA (US)]][[Category:Oluwatobi Olabiyi of Arlington VA (US)]]

[[:Category:Erik T. Mueller of Chevy Chase MD (US)|Erik T. Mueller of Chevy Chase MD (US)]][[Category: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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### Potential Applications
* Chatbots for customer service
* Virtual assistants for natural language interaction
* Automated messaging systems for communication platforms

### 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

### 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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