Template Bootstrapping for Domain-Adaptable NLG
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Solution Overview
Problem
Existing natural language generation (NLG) systems require extensive human input and time to generate sentence templates for new domains, making them inefficient and labor-intensive.
Innovation Solution
A bootstrapping process that uses a set of original templates to identify and modify candidate sentences from a large corpus, reducing the need for human intervention by generating a large body of sentence templates quickly through a similarity measure-based approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If rule-based NLG systems are used to generate natural language text, then the quality of generated text is high and grammatically correct, but extensive human investment and time are required to create domain-dependent rules
Solution Approach 1:
The system performs preliminary action by automatically generating domain-dependent rules from corpus data before text generation is needed. The rule generation module creates rules in advance by analyzing patterns in the corpus, eliminating the need for manual rule creation while maintaining high text quality.
Solution Approach 2:
The NLG system performs self-service by automatically generating its own domain-dependent rules from the corpus without requiring external human expertise. The rule generation module enables the system to self-configure for new domains by learning patterns directly from data.
2Extent of automation
If statistical NLG systems are used to reduce human investment, then less domain expertise is required, but the generated text may be shorter and less natural
Solution Approach 1:
The system merges statistical NLG approaches with template-based generation. The rule generation module creates structured templates from corpus data, combining the automation benefits of statistical methods with the grammatical correctness and naturalness of template-based generation.
Solution Approach 2:
The system changes parameters by dynamically selecting among multiple alternative generations at each decision point. The decision model adjusts generation parameters based on the specific context and input data, allowing the system to maintain naturalness while reducing human investment.
3Reliability
If domain-dependent rules are hand-crafted for rule-based NLG systems, then linguistic and domain expertise are utilized, but intensive human investment is required
Solution Approach 1:
The system replaces the mechanical process of manual rule creation with an automated rule generation module. This module uses computational algorithms to analyze corpus data and generate rules automatically, substituting human manual work with machine-based processing while maintaining linguistic quality.
Solution Approach 2:
The rule generation module creates copies of effective rules by identifying and replicating successful patterns from the corpus. Instead of hand-crafting each rule individually, the system copies and adapts proven linguistic patterns from the training data, improving productivity while preserving linguistic quality.
4Manufacturing precision
If templates are used in NLG systems to structure syntactic output, then grammatical correctness is improved, but the text becomes less variable and less interesting
Solution Approach 1:
The system introduces dynamics by selecting among multiple alternative templates and generations at each decision point. The decision model dynamically chooses the most appropriate template variation based on the input data and context, maintaining grammatical correctness while increasing variability and interest.
Solution Approach 2:
The rule generation module creates universal templates that can function across multiple contexts and domains. These templates are designed to be adaptable to different situations while maintaining grammatical structure, allowing the system to achieve both correctness and versatility.
Data Source
AI summary
The present invention relates to a system and method for bootstrapping templates for use in natural language sentence generation. More specifically, the present invention relates to identifying a set of candidate sentences from a large corpus based on a set of original templates by using a similarity measure. The set of candidate sentences are then processed or cleaned to generate a set of templates for use in natural language sentence generation.