Dialogue State Modeling for Human-Like Natural Language Generation
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Solution Overview
Problem
Existing dialogue systems lack the ability to efficiently generate natural language content that imitates human-like conversation without requiring a large number of predefined rules and templates.
Innovation Solution
A system that generates and maintains a three-view dialogue state representation, continuously updated during discourse, using a hybrid approach of automatically generated rules and user input-based templates, to produce natural language output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed number of predefined behaviors and rules are used in dialogue systems, then the system structure is simple and easy to implement, but the system lacks flexibility and cannot efficiently generate human-like natural language content
Solution Approach 1:
The patent implements dynamic behavior generation by transitioning from fixed predefined behaviors to dynamically created behaviors during discourse. The system generates new behaviors on-the-fly based on discourse analysis, allowing the dialogue system to adapt to varying conversational contexts without requiring a large static rule set, thus resolving the contradiction between flexibility and complexity.
Solution Approach 2:
The system performs self-service by automatically generating its own behaviors and rules through discourse analysis. Rather than requiring external programmers to define all possible behaviors, the system learns and creates its own behavioral patterns during interaction, reducing the burden of manual rule creation while increasing adaptability.
2Productivity
If manually written rules and templates are used for each behavior, then the system can provide precise control over output, but the development time and effort required is extensive
Solution Approach 1:
The system automatically generates rules and templates through discourse analysis between user and agent. By observing and analyzing actual discourse patterns, the system creates its own behavioral rules and templates without requiring extensive manual programming, thereby dramatically reducing development time while maintaining output quality.
Solution Approach 2:
The system performs preliminary discourse analysis to generate rules and templates before they are needed for actual behavior execution. By pre-processing discourse data and creating rules in advance, the system reduces the time required for rule development during deployment and operation.
3Productivity
If automatically generated rules are used for all discourse levels, then development time is reduced, but the quality of high level discourse generation may suffer
Solution Approach 1:
The patent applies different rule generation strategies to different discourse levels. For low level discourse (data collection), automatically generated rules are used to maintain productivity. For high level discourse, annotation-based templates are used to ensure output quality. This localized application of different methods resolves the contradiction between speed and quality.
Solution Approach 2:
The system changes the parameter of rule source based on discourse level. Annotation-based templates are weighted more heavily for high level discourse while automatically generated rules are used for low level discourse. This dynamic parameter adjustment allows the system to optimize both productivity and quality for different types of discourse.
Data Source
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AI summary
A system that generates natural language content. The system generates and maintains a dialogue state representation having a process view, query view, and data view. The three-view dialogue state representation is continuously updated during discourse between an agent and a user, and rules can be automatically generated based on the discourse. Upon a content generation event, an object description can be generated based on the dialogue state representation. A string is then determined from the object description, using a hybrid approach of the automatically generated rules and other rules learned from annotation and other user input. The string is translated to text or speech and output by the agent. The present system also incorporates learning techniques, for example when ranking output and processing annotation templates.