Multi-Turn Language Understanding via Statistical Model Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current language understanding systems, particularly in multi-turn scenarios, rely on deterministic rules and lack accurate and reliable information due to limited data, leading to complexity and rule breakage as new inputs are received.
Innovation Solution
A statistical modeling framework combining single-turn and multi-turn models using artificial neural networks, Bayesian classifiers, and genetically derived algorithms to facilitate contextual language understanding, enabling flexible handling of multi-turn scenarios without disrupting single-turn models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deterministic rules are used for language understanding in multi-turn scenarios, then the system can operate with limited data, but the accuracy and reliability of intent prediction deteriorates
Solution Approach 1:
The patent replaces deterministic rule-based systems with statistical machine learning models (neural networks, Bayesian classifiers) that automatically learn patterns from data, eliminating the need for manual rule creation and maintenance while improving accuracy in multi-turn scenarios
Solution Approach 2:
The system uses genetically derived algorithms that enable the model to self-optimize and adapt to new inputs automatically, reducing the need for manual rule updates and maintaining reliability as new language patterns emerge
2Adaptability or versatility
If rule-based systems are used to handle new language inputs, then the system structure remains simple, but the rules become complex and breakable as new inputs are received
Solution Approach 1:
The patent transforms the static rule-based approach into a dynamic statistical model where parameters (weights and biases) are continuously optimized through genetic algorithms, allowing the system to adapt to new language inputs by changing model parameters rather than adding complex rules
Solution Approach 2:
The system transitions from static deterministic rules to dynamic statistical models that can adapt their behavior based on learned patterns, enabling flexible handling of new multi-turn scenarios without requiring manual rule updates
3Reliability
If single-turn models are used for language understanding, then the model structure remains simple, but the accuracy in multi-turn scenarios deteriorates due to lack of contextual information
Solution Approach 1:
The patent divides the language understanding system into separate single-turn and multi-turn model components, each optimized for their specific function, allowing the system to leverage contextual information in multi-turn scenarios while maintaining the simplicity of single-turn processing where applicable
Solution Approach 2:
The system merges single-turn and multi-turn model predictions through a combination layer, integrating contextual information from multi-turn models with direct language understanding from single-turn models to achieve improved overall accuracy
Data Source
AI summary
Methods and systems are provided for contextual language understanding. A natural language expression may be received at a single-turn model and a multi-turn model for determining an intent of a user. For example, the single-turn model may determine a first prediction of at least one of a domain classification, intent classification, and slot type of the natural language expression. The multi-turn model may determine a second prediction of at least one of a domain classification, intent classification, and slot type of the natural language expression. The first prediction and the second prediction may be combined to produce a final prediction relative to the intent of the natural language expression. An action may be performed based on the final prediction of the natural language expression.