Text Style Variation via Neural Style Transfer
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Solution Overview
Problem
Conventional machine learning models, such as convolutional neural networks, fail to efficiently generate variations in text style to cater to different viewer responses, as they do not effectively handle variations in text tone or mood.
Innovation Solution
A machine-learned style transfer model is employed to generate text variations by applying one or more style transfer models to reference text, using an encoder-decoder architecture to retain the textual content while stylizing it in different styles, allowing for automatic and computationally efficient generation of multiple text styles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional neural networks are used to process digital content items, then text generation is achieved, but text style variations cannot be effectively handled
Solution Approach 1:
The patent segments text into two distinct components: textual content (semantic meaning) and text style (tone, mood, persuasion method). This is achieved through separate encoding pathways in the neural network - one for content representation and another for style representation. The segmentation allows independent manipulation of style while preserving content integrity, enabling effective style variations that cater to different viewer preferences.
Solution Approach 2:
The patent introduces style embedding vectors as intermediary elements that mediate between the content representation and the final text generation. These style embeddings act as a bridge, allowing the model to inject specific stylistic characteristics (such as authority, scarcity, or friendliness) into the generated text without altering the underlying semantic content. This intermediary mechanism enables reliable style transfer while maintaining content accuracy.
2Productivity
If manual text style variation is performed, then viewer response effectiveness improves, but automation and efficiency are reduced
Solution Approach 1:
The patent implements a universal style transfer model that can handle multiple text styles through a single architecture. The model is trained on diverse style categories (authority, scarcity, friendliness, etc.) and can generate variations across all these styles using the same underlying network structure. This multi-functionality achieves high productivity without requiring separate manual processes for each style, as the model automatically adapts to different style requirements.
Solution Approach 2:
The patent controls text style variations by changing style embedding parameters rather than restructuring the entire model architecture. By adjusting which style embedding is applied, the system can efficiently generate different text styles (authority-based, scarcity-based, friendliness-based) from the same content representation. This parameter-based control achieves high productivity with manageable complexity, as style changes are achieved through parameter selection rather than architectural redesign.
Data Source
AI summary
An online system generates a set of content item variations for a reference content item that include different styles of text for the content item. The different styles of text are generated by applying machine-learned style transfer models, for example, neural network based models to reference text of the reference content item. The text variations retain the textual content of the reference text but are synthesized with different styles. The online system can provide the content item variations to users on an online experimental platform to collect user interaction information that may indicate how users respond to different styles of text. The online system or the content providers can effectively target users with content items that include the style of text the users respond to based on the collected information.