Decoupled Neural Memory for Private Fair Dialogue Retraining
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Solution Overview
Problem
Existing neural dialogue agents struggle to utilize structured data from knowledge bases effectively, leading to biased and privacy-compromised responses, especially when using sensitive contextual customer data for training and re-training neural network models.
Innovation Solution
A system and method that decouples the trained neural network from its memory, using a contextual trainer to translate contextual data into keys, generate gradients, and create a Fair Region vector to adapt responses without exposing sensitive data, ensuring privacy and fairness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contextual customer data is integrated into the knowledge base to improve model accuracy, then the accuracy of automated dialogue responses is improved, but customer privacy is compromised and bias is introduced
Solution Approach 1:
The patent segments the training process into offline model training with contextual data and online inference without contextual data. The neural network model is trained offline using contextual customer data from knowledge bases to learn patterns and improve accuracy. During online deployment, the trained model processes queries without accessing actual contextual data, thus maintaining privacy while benefiting from offline learning.
Solution Approach 2:
The patent introduces an intermediary mechanism where the neural network model acts as a mediator between contextual data and dialogue responses. The model learns from contextual data during training but processes only the dialogue queries during deployment, preventing direct exposure of sensitive customer information while still utilizing the learned patterns for accurate responses.
2Adaptability or versatility
If the entire neural network memory architecture is retrained to adapt to new contextual data, then the model can incorporate new observations, but the computational complexity and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by training the neural network model offline before deployment. The model is pre-trained with contextual data and patterns in advance, so that during online operation, it can directly process queries without requiring retraining. This preliminary training phase captures all necessary knowledge, enabling the model to adapt to new situations through inference rather than retraining.
3Productivity
If contextual data is stored and processed in cloud-based services, then the model can utilize comprehensive customer information, but data security and privacy protection are compromised
Solution Approach 1:
The patent extracts contextual data from the cloud-based service environment and uses it exclusively during offline training. The actual customer contextual information is taken out of the online processing pipeline, ensuring it never暴露 to cloud-based services during deployment. Only the trained model parameters remain in the cloud, while sensitive data stays isolated in the offline training environment.
Data Source
AI summary
Systems and methods are provided herein for utilizing a knowledge base to improve online automated dialogue responses based on machine learning models. Contextual customer data stored in external memory may be used for retraining a machine learning model to incorporate new observations into the model and to reduce bias and/or improve fairness in associated automated responses without having to retrain an entire memory architecture. The disclosed technology may improve the accuracy of machine learning models by using potentially private contextual customer data to inform the model while eliminating the ability of an intruder to access such data when the model is utilized in cloud-based services.