Narration Generator Using Context-Free Grammar Templates
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Solution Overview
Problem
Current data analysis tools often require expertise to interpret graphs and provide insights, and they either produce grammatically incorrect machine translations or merely generate graphs without comprehensive textual summaries, failing to effectively convey complex data trends to non-expert users.
Innovation Solution
A narration generator system that accepts various data types, analyzes user queries, and generates grammatically accurate, human-readable textual summaries along with graphical representations, using a combination of intent parsing, sentence struct models, and recurrent neural networks to produce context-free grammar for coherent and consistent sentences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated translation tools are used to convert data insights to text, then productivity is improved, but grammatical accuracy and natural language quality deteriorate
Solution Approach 1:
The patent introduces an intermediary grammatical correction system that sits between the automated translation component and the final output. This intermediary layer includes grammar checking modules and natural language processing components that refine the machine-translated text, correcting grammatical errors and improving fluency while preserving the automated generation efficiency.
Solution Approach 2:
The patent replaces simple machine translation mechanisms with advanced natural language generation systems that use contextual understanding and grammatical rules. Instead of direct mechanical translation, the system employs sophisticated language models that generate text resembling human-written content, thereby improving grammatical accuracy while maintaining automation.
2Ease of operation
If comprehensive textual summaries are generated for data trends, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent divides the narration generation system into distinct modular components: data analysis modules, template selection modules, parameter extraction modules, and text generation modules. Each module performs a specific function, making the overall complex system manageable and maintainable while delivering comprehensive textual summaries that ease data interpretation for users.
Data Source
AI summary
A narrative response generator receives a user data query specifying variables and data sources from which to extract information desired by a user. The narrative response presents the information desired by the user in a non-textual format such as graphs and a textual format such as one or more paraphrases that are automatically generated by a sentence struct model. The sentence struct model generates context free grammar (CFG) which provides templates for generating word sequences that contain natural language words and placeholders. The placeholders are replaced with values obtained from the user data query for generating grammatically-accurate, complete paraphrases. The narrative response may additionally include information extracted from external data sources.