Natural Language Generation from Keywords Using Template Vectors
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Solution Overview
Problem
Existing natural language generation systems face challenges in generating realistic sentences from a set of keywords, particularly when keywords are unordered and come from structured inputs like tables or knowledge bases, as they fail to capture semantic, lexical, and syntactic aspects effectively, leading to inefficiencies in learning deep syntactic structures and scalability issues.
Innovation Solution
A system that generates natural language content by creating keyword vectors and template vectors using language tags, including part-of-speech (POS) tags, allowing for context-based contributions and probability calculations to guide the generation process, reducing the burden of learning deep syntactic structures and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing natural language generation systems are used to generate sentences from unordered keywords, then the system can produce text output, but the system fails to capture semantic, lexical, and syntactic aspects effectively, resulting in poor generation quality
Solution Approach 1:
The system segments the generation process into distinct components: keyword embedding, template selection, and sentence assembly. Each component handles specific aspects (semantic representation, syntactic structure, and combination logic), allowing the system to maintain high generation quality while effectively processing unordered keywords through specialized sub-modules.
Solution Approach 2:
The patent introduces templates as intermediary structures that bridge unordered keywords and grammatically correct sentences. Templates serve as mediators that provide the syntactic framework, while keywords are mapped to appropriate positions within the template structure, enabling the system to generate quality text from unordered input.
2Adaptability or versatility
If the system learns deep syntactic structures from scratch to handle unordered keywords, then it can improve adaptation to keyword-based input, but training time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-defining template structures that encode syntactic rules and grammatical patterns. Instead of learning syntactic structures from scratch during training, the templates are prepared in advance, allowing the model to focus learning on mapping keywords to template positions rather than learning grammar rules, significantly reducing training time.
Solution Approach 2:
The patent changes the problem parameters by transforming the input from unordered keywords to a structured representation that includes keyword-template mappings. This parameter transformation allows the system to leverage pre-existing linguistic knowledge embedded in templates rather than requiring extensive training to learn syntactic relationships.
3Ease of operation
If rule-based controllable styling approaches are used to transform input text, then the approach is intuitive and controllable, but it is not feasible to scale to various domains, data formats, and template patterns
Solution Approach 1:
The system achieves universality by designing a template-based framework that can handle multiple domains, data formats, and template patterns through a single unified approach. The templates are designed to be domain-agnostic and can be applied across different contexts, allowing the system to maintain controllability while scaling to various applications without requiring domain-specific rule sets.
Solution Approach 2:
The patent introduces dynamics by making the template selection and keyword mapping processes adaptive rather than static. The system dynamically selects appropriate templates based on the input keywords and their semantic relationships, and dynamically maps keywords to template positions based on learned associations, enabling scalability across domains while maintaining operational control.
4Manufacturing precision
If templates are created from sentences of a large unlabeled text corpus, then the system can leverage abundant data to improve generation accuracy, but the process of creating and processing templates increases system complexity
Solution Approach 1:
The system extracts only the essential syntactic and structural patterns from the large text corpus to create templates, rather than processing entire sentences. By extracting and retaining only the template structures (syntactic frameworks) without the full sentence content, the system leverages abundant data for improved accuracy while reducing template processing complexity to manageable levels.
Data Source
AI summary
An embodiment of the present invention generates natural language content from a set of keywords in accordance with a template. Keyword vectors representing a context for the keywords are generated. The keywords are associated with language tags, while the template includes a series of language tags indicating an arrangement for the generated natural language content. Template vectors are generated from the series of language tags of the template and represent a context for the template. Contributions from the contexts for the keywords and the template are determined based on a comparison of the series of language tags of the template with the associated language tags of the keywords. One or more words for each language tag of the template are generated to produce the natural language content based on combined contributions from the contexts for the keywords and the template.