Unsupervised Text Transformation via Neural Stylistic Parameters
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Solution Overview
Problem
Existing natural language generation systems are unable to produce variable output based on user-desired tunable specifications, cannot handle input in various forms of text and specifications, and are not readily extendable.
Innovation Solution
The development of unsupervised tunable stylized text transformations using neural networks to identify and modify amendable portions of input text based on stylistic parameters, enabling the generation of stylized output text through processing with embedding, encoder, and decoder layers, including bi-directional LSTMs and softmax regression, without requiring labeled data for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing natural language generation systems are used, then text generation function is provided, but the systems cannot produce variable output based on user-desired tunable specifications
Solution Approach 1:
The patent applies parameter changes by introducing a set of tunable stylistic parameters that control the output characteristics of the text generation system. These parameters allow users to adjust properties such as formality, sentiment, and style without changing the underlying system architecture, enabling variable output based on user specifications while maintaining system stability.
Solution Approach 2:
The system implements dynamics by making the output characteristics adjustable and adaptable through the stylistic parameters. The text generation process can dynamically modify its behavior based on the selected parameters, allowing the same system to produce different styles of text (e.g., formal vs. informal, positive vs. negative sentiment) without requiring multiple separate systems.
2Adaptability or versatility
If existing natural language generation systems are used, then text generation is performed, but the systems cannot take input in the form of variable form of text and variable set of specifications
Solution Approach 1:
The patent applies universality by designing a text generation system that can handle multiple input forms and specification types through a unified framework. The system accepts variable forms of text input and variable sets of stylistic specifications using the same core processing mechanism, eliminating the need for separate processing paths for different input types.
3Adaptability or versatility
If existing natural language generation systems are used, then text output is generated, but the systems are generally not readily extendable
Solution Approach 1:
The patent applies segmentation by separating the text generation system into distinct functional components: the core generation engine and the stylistic parameter control layer. This modular architecture allows new stylistic parameters or input types to be added by extending the parameter layer without modifying the core system, making the system readily extendable while maintaining architectural simplicity.
Data Source
AI summary
Methods, systems, and computer program products for unsupervised tunable stylized text transformations are provided herein. A computer-implemented method includes identifying amendable portions of input text by processing at least a portion of the input text using at least one neural network; determining stylistic text modifications to the amendable portions of the input text, the text modifications encompassing a set of stylistic parameters, wherein said determining comprises processing at least a portion of the set of stylistic parameters using at least one neural network; generating a stylized output set of text by transforming at least a portion of the input text, wherein said transforming comprises modifying at least one of the amendable portions of the input text via at least one of the stylistic text modifications encompassed by the set of stylistic parameters; and outputting the stylized output set of text to at least one user.