Multilingual Text Rewriting Model for Zero-Shot Formality Transfer
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Solution Overview
Problem
Conventional multilingual translation technologies face challenges in effectively conveying style and formality across languages, often resulting in 'accidental translations' and significant resource tradeoffs between model size and memory costs.
Innovation Solution
A model-based approach for multilingual text rewriting that leverages a large pretrained multilingual model, fine-tuned for general-purpose multilingual attribute transfer, enabling zero-shot formality-sensitive translation and adaptation to different domains or locales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If zero-shot or few-shot approaches are used in limited translation instances, then model training efficiency is improved, but translation quality and effectiveness in multilingual settings deteriorate
Solution Approach 1:
The model is pre-trained on large-scale multilingual corpora before being fine-tuned on specific translation tasks. This preliminary action provides the model with broad linguistic knowledge and cross-lingual understanding, enabling it to perform effectively in multilingual settings while maintaining training efficiency through subsequent fine-tuning on limited labeled data.
Solution Approach 2:
The patent employs a multilingual language model that can perform multiple functions including translation, style transfer, and text rewriting across many language pairs. This universal model architecture allows the system to handle diverse multilingual translation tasks with a single model, improving both efficiency and quality by leveraging transfer learning from pre-training on extensive multilingual data.
2Use of energy by moving object
If conventional translation models are used, then resource consumption is reduced, but ability to convey style and formality across languages deteriorates
Solution Approach 1:
The patent introduces style vectors as additional parameters to the translation model, enabling control over stylistic attributes such as formality, tone, and register. By modifying the model's output parameters to include style dimensions, the system can convey style and formality across languages while using efficient fine-tuning approaches that do not require retraining entire large-scale models.
Solution Approach 2:
The patent uses style vectors as intermediary representations that mediate between the input text and translated output. These style vectors capture stylistic properties and serve as a bridge, allowing the model to transfer style attributes across languages without requiring complex architectural changes or excessive computational resources.
3Reliability
If larger models are used for multilingual translation, then translation quality is improved, but memory costs increase significantly
Solution Approach 1:
The model undergoes pre-training on large-scale multilingual corpora using available computational resources, establishing a strong foundation of linguistic knowledge. This preliminary action enables the model to achieve high translation quality while allowing subsequent fine-tuning to be performed efficiently on smaller hardware configurations, reducing memory costs for deployment.
Solution Approach 2:
The patent uses fine-tuned copies of the pre-trained multilingual model for specific translation tasks and language pairs. Instead of deploying a single large model for all tasks, the system creates specialized copies through efficient fine-tuning, reducing memory requirements while maintaining translation quality for specific use cases.
Data Source
AI summary
The technology provides a model-based approach for multilingual text rewriting that is applicable across many languages and across different styles including formality levels or other textual attributes. The model is configured to manipulate both language and textual attributes jointly. This approach supports zero-shot formality-sensitive translation, with no labeled data in the target language. An encoder-decoder architectural approach with attribute extraction is used to train rewriter models that can thus be used in “universal” textual rewriting across many different languages. A cross-lingual learning signal can be incorporated into the training approach. Certain training processes do not employ any exemplars. This approach enables not just straight translation, but also the ability to create new sentences with different attributes.