Template-Based Text Generation with Grammatical Tags
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing natural language generation techniques, such as template-based and linguistic-based methods, face limitations in flexibility and complexity, struggling to account for grammatical context, style, and tone variations, requiring numerous templates or complex algorithms, and often fail to generate grammatically correct text efficiently.
Innovation Solution
The use of templates with grammatical tags that indicate actor characteristics, such as number and gender, allows for the automatic generation of human-readable text by accessing and filling placeholders with contextually appropriate text, and a post-generation analysis to correct grammatical errors like punctuation and capitalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If template-based natural language generation techniques are used, then text generation is simplified, but the system lacks flexibility in accounting for grammatical context, style, and tone variations
Solution Approach 1:
The patent introduces grammatical tags as parameters within templates that can be dynamically changed based on context. These tags specify grammatical properties (such as number, gender, person) that allow the same template to generate grammatically correct text for different contexts, styles, and tones without requiring separate templates for each variation.
Solution Approach 2:
The patent creates universal templates that can serve multiple functions by incorporating grammatical tags. A single template with appropriate tags can generate text for various grammatical contexts, styles, and tones, eliminating the need for numerous specialized templates while maintaining adaptability across different scenarios.
2Manufacturing precision
If numerous templates are used to account for grammatical variations, then grammatical accuracy improves, but system complexity increases
Solution Approach 1:
Instead of creating numerous templates for different grammatical scenarios, the patent uses parameter changes within a unified template structure. Grammatical tags act as parameters that can be adjusted based on the desired output, allowing the system to maintain high grammatical accuracy while avoiding the complexity of managing multiple templates.
Solution Approach 2:
The patent develops universal templates that can handle multiple grammatical scenarios through the use of grammatical tags. This approach reduces the total number of templates needed while maintaining the ability to generate grammatically correct text for various contexts, thereby reducing system complexity.
3Adaptability or versatility
If linguistic-based generation methods are used to improve flexibility, then adaptability increases, but the algorithms become more complex
Solution Approach 1:
The patent introduces grammatical tags as an intermediary between the template structure and the final generated text. These tags serve as a simplified interface that captures essential grammatical information without requiring complex linguistic algorithms, thereby maintaining flexibility while reducing algorithmic complexity.
Solution Approach 2:
The patent uses parameter changes in the form of grammatical tags to achieve flexibility without complex algorithms. By allowing templates to dynamically adjust their parameters based on input data, the system gains adaptability while avoiding the need for sophisticated linguistic processing algorithms.
4Manufacturing precision
If post-generation analysis is performed to correct grammatical errors, then grammatical accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary grammatical analysis during the text generation process itself by using grammatical tags to guide template instantiation. This preliminary action ensures grammatical correctness is built into the generated text from the start, reducing or eliminating the need for time-consuming post-generation correction passes.
Solution Approach 2:
The patent enables the text generation system to self-correct grammatical issues by incorporating grammatical tags that automatically adjust the generated text based on the input parameters. The system serves its own grammatical needs through the template-tag mechanism without requiring external post-processing correction.
Data Source
AI summary
Some embodiments relate to identifying grammatical errors in text that has been automatically generated from a template. The text may be scanned to identify, for example, errors in punctuation, spacing, and capitalization. When an error in the text is identified, it may be corrected. Some embodiments relate to automatically formatting lists in text that is generated from a template. For example, a template may include a tag that specifies a list of elements. A formatting parameter may be determined that specifies whether to format the list as an enumerated list or a textual sentence. Output text may then be generated that includes the list in the proper format.