Graph-Based NLG Intent Traversal for Scalable Domain Adaptation
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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 employed to organize intents, allowing for a common NLG platform that can be adjusted and improved to meet user needs, with chooser, structurer, and realizer codes modularly integrated to generate narratives of varying sizes and contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a common NLG platform is used across multiple domains, then scalability is improved, but flexibility to adapt to different data sets and use cases deteriorates
Solution Approach 1:
The NLG system is segmented into distinct modular components: a graph data structure module for organizing intents, a chooser code module for selecting intents, a structurer code module for organizing content, and a realizer code module for generating narratives. This segmentation allows each module to be independently configured and optimized for different domains while maintaining a common platform architecture.
Solution Approach 2:
The graph data structure is designed to be dynamically configurable, allowing intents and their relationships to be adjusted based on specific domain requirements. The system can adapt the graph structure, chooser logic, and structuring rules to match different data sets and use cases without requiring complete system redesign.
2Productivity
If the NLG system is fine-tuned for specific use cases, then performance is improved, but scalability to other domains deteriorates
Solution Approach 1:
The graph data structure serves as a universal framework that can represent intents across multiple domains. By using a domain-general graph structure that can be populated with domain-specific intents and relationships, the system achieves both specialized performance for specific use cases and scalability to other domains through the same underlying architecture.
3Adaptability or versatility
If a graph data structure is used to organize intents, then adaptability improves, but device complexity deteriorates
Solution Approach 1:
The graph data structure acts as an intermediary layer between the raw data sets and the NLG generation process. It provides a standardized interface for representing intents and their relationships, simplifying the complexity by abstracting away the underlying data structure details from the chooser, structurer, and realizer modules.
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. The nodes (1) represent corresponding intents so that different nodes represent different corresponding intents, (2) are associated with corresponding analytics for execution to evaluate a given node's corresponding intent and generate a result that represents an evaluation of that node's corresponding intent, and (3) are associated with one or more links with one or more of the nodes to define relationships among the intents. A processor traverses the graph data structure based on defined criteria and a plurality of the links to (1) choose which nodes are to be evaluated and (2) determine content for expression in the narratives based on execution of the chosen nodes' corresponding analytics against the structured data.