Document Planner Schema Optimization for NLG
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Solution Overview
Problem
Natural language generation systems face computational inefficiencies in document planning due to the need for exhaustive searches among numerous combinations of message selection, partitioning, and ordering, and often result in non-unique document plans, making it challenging to generate optimal text structures.
Innovation Solution
A document planner employing top-down planning with schemas and bottom-up narrative optimization, using optimization specifications to control discourse features and message ordering, to generate a document plan that can be processed by a microplanner for natural language text creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive search is used to select and order messages in document planning, then completeness of message combinations is improved, but computational complexity increases significantly
Solution Approach 1:
The document planning process is divided into distinct phases: message selection based on schema requirements, message partitioning into logical groups, and message ordering within each partition. This segmentation allows the system to handle each aspect separately rather than exhaustively searching all possible combinations simultaneously, reducing computational complexity while maintaining completeness.
Solution Approach 2:
The schema defines message selection criteria and document structure requirements in advance, before the actual document planning occurs. By pre-specifying which messages are needed and how they should be organized, the system avoids exhaustive search during execution, as the framework for message selection and ordering is already established.
2Manufacturing precision
If multiple message combinations are evaluated for document planning, then quality of document structure is improved, but generation time increases
Solution Approach 1:
The system partitions messages into logical groups based on their thematic or functional relationships, then orders each partition separately. This segmentation allows quality optimization within each partition without requiring exhaustive evaluation of all possible global arrangements, reducing generation time while maintaining document structure quality.
Solution Approach 2:
The schema specifies the essential messages and structure requirements needed for a quality document plan. The system evaluates message combinations sufficient to meet these schema requirements rather than exhaustively evaluating all possible combinations, achieving adequate quality without excessive generation time.
3Manufacturing precision
If document plan optimization is performed to ensure uniqueness, then document plan quality is improved, but computational resources required increase
Solution Approach 1:
The schema pre-defines the document structure, required messages, and ordering constraints before optimization begins. This preliminary framework guides the optimization process, allowing the system to achieve unique, high-quality document plans without exhaustively searching all possible plans, thus reducing computational resource requirements.
Solution Approach 2:
The document planning system uses the schema's inherent structure and constraints to guide its own optimization process. The schema effectively serves as a self-imposed framework that directs message selection and ordering, reducing the need for external computational resources to enforce structure requirements.
Data Source
AI summary
Methods, apparatuses, and computer program products are described herein that are configured to be embodied as and/or performed by a document planner. In some example embodiments, a method is provided that comprises selecting a schema based on one or more messages available in a message store and using the selected schema and one or more messages available in the message store to generate a document plan. The schema of this embodiment may be defined by a specification containing one or more queries for selecting one or more messages, one or more messages, and/or one or more predefined phrases to instantiate a document plan. The method of this embodiment may also include applying an optimization specification to the document plan to generate an optimized document plan.