Graph-Based NLG Architecture for Scalable Domain Customization
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Solution Overview
Problem
Existing natural language generation (NLG) systems lack flexibility and scalability, struggling to adapt to different data sets across multiple domains while maintaining the ability to fine-tune operations for specific use cases.
Innovation Solution
A graph data structure is used to organize intents, allowing for modular chooser and structurer codes that can be configured to generate narratives of varying sizes and adapt to user-specific needs, with the ability to identify follow-up information through linked nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a common NLG platform is used across multiple domains with different data sets, then scalability is improved, but the ability to fine-tune operations for specific use cases deteriorates
Solution Approach 1:
The NLG system is segmented into distinct modular components: a graph data structure for organizing intents, chooser code for selecting content, structurer code for organizing narratives, and realizer code for generating natural language. This segmentation allows each component to be independently configured and fine-tuned for specific domains while maintaining a common platform architecture, thereby resolving the contradiction between scalability and fine-tuning capability.
Solution Approach 2:
The system employs dynamic configuration where the graph data structure, chooser code, structurer code, and realizer code can be adaptively adjusted based on specific domain requirements. This dynamic nature enables the common platform to be fine-tuned for different use cases without sacrificing its scalability across multiple domains.
2Loss of information
If the NLG system traverses the entire graph data structure to generate narratives, then completeness of content is improved, but processing time deteriorates
Solution Approach 1:
The graph data structure is pre-organized with nodes representing different intents and relationships between them before narrative generation begins. This preliminary structuring allows the chooser code to efficiently traverse only relevant portions of the graph based on user input, rather than processing the entire graph, thus maintaining content completeness while reducing processing time.
Solution Approach 2:
The chooser code extracts and selects only the relevant nodes and content from the graph data structure that are necessary to answer the user's question or fulfill the specific intent, rather than including all possible content. This extraction process ensures content completeness for the specific use case while significantly reducing processing time by avoiding unnecessary traversal of unrelated graph portions.
3Adaptability or versatility
If multiple choosers and structurers are implemented for different domains, then adaptability to specific use cases is improved, but system complexity deteriorates
Solution Approach 1:
The system employs universal chooser code and structurer code that can function across multiple domains through configuration rather than requiring completely separate implementations for each domain. These universal components can be adapted to different domains by configuring the graph data structure and parameters, thereby achieving domain-specific customization without proportionally increasing system architecture complexity.
Data Source
AI summary
Natural language generation technology is disclosed that applies artificial intelligence to structured data to determine content for expression in natural language narratives that describe the structured data. A graph data structure is employed, where the graph data structure comprises a plurality of nodes. Each of a plurality of the nodes (1) represents a corresponding intent so that a plurality of different nodes represent different corresponding intents and (2) is associated with one or more links to one or more of the nodes to define relationships among the intents. A processor executes chooser code, structurer code, and realizer code to generate the natural language narratives. The chooser code determines content for expression in a natural language narrative based on a traversal of the graph data structure based on defined criteria and a plurality of the links, wherein the traversal operates to choose a plurality of the nodes for evaluation with respect to the structured data to derive content for expression in that natural language narrative.