Independent Gate Recurrent Neural Network for Dialogue Response Generation
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Solution Overview
Problem
Conventional digital dialogue systems suffer from inaccuracies, inefficiencies, and lack of domain-adaptation flexibility due to reliance on domain-specific rules and feature engineering, failing to account for contextual and sequential nature of conversations.
Innovation Solution
The system employs an independent gate context-dependent additive recurrent neural network that generates dialogue responses based on word-embeddings and global contextual information, eliminating the need for domain-specific rules and feature engineering, and enabling efficient and flexible operation across various domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based and feature-based systems are used to generate dialogue responses, then the system can provide structured responses, but the system requires significant processing power, time, and storage resources
Solution Approach 1:
The patent replaces rule-based and feature-engineering mechanical systems with a neural network-based learning system. The neural network automatically learns dialogue patterns and generates responses without requiring manual rule creation or feature extraction, thereby reducing computational overhead while maintaining response quality.
Solution Approach 2:
The neural network system is self-training and self-adapting, automatically improving its dialogue capabilities through continuous learning from interactions. This eliminates the need for manual rule updates and feature engineering, reducing long-term computational resources required for system maintenance and adaptation.
2Measurement precision
If domain-specific rules and features are engineered for each conversation type, then the system can handle specific domains accurately, but the system cannot adapt to other domains and requires large storage for multiple variations
Solution Approach 1:
The patent implements a universal neural network system that can handle multiple domains through a single model. The network learns general dialogue patterns that apply across different domains (food, technology, sports, etc.) without requiring separate rule sets or feature engineering for each domain, enabling both accuracy and adaptability.
Solution Approach 2:
The system adapts to different domains by changing its learned parameters through continuous training on domain-specific data. The neural network dynamically adjusts its internal representations and weights based on the conversation domain, allowing accurate domain-specific responses without requiring separate system configurations.
3Ease of operation
If conventional systems continuously update sequential context, then they can maintain conversation flow, but they forget previous context when topics change quickly
Solution Approach 1:
The patent implements a dynamic context management system where the neural network selectively attends to different parts of the dialogue history based on current conversation needs. The system dynamically adjusts which contextual information to retain or discard, maintaining relevant context while adapting to topic changes without rigid sequential updating.
Solution Approach 2:
The system uses feedback mechanisms where the neural network continuously evaluates the relevance of previous context based on current conversation state. This allows the system to maintain conversation flow by retaining useful context while automatically discarding irrelevant information when topics change, preventing context loss through intelligent feedback-driven selection.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for generating dialogue responses based on received utterances utilizing an independent gate context-dependent additive recurrent neural network. For example, the disclosed systems can utilize a neural network model to generate a dialogue history vector based on received utterances and can use the dialogue history vector to generate a dialogue response. The independent gate context-dependent additive recurrent neural network can remove local context to reduce computation complexity and allow for gates at all time steps to be computed in parallel. The independent gate context-dependent additive recurrent neural network maintains the sequential nature of a recurrent neural network using the hidden vector output.