Text data augmentation method and apparatus
By using the diffusion and de-diffusion processes of the text diffusion model, the problem of deviation from the original text in text data augmentation is solved, and the generated data-augmented text conforms to the distribution of the original text, thus improving the model training effect.
CN116108810BActive Publication Date: 2026-07-10SHENZHEN XUMI YUNTU SPACE TECH CO LTD
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENZHEN XUMI YUNTU SPACE TECH CO LTD
- Filing Date
- 2023-02-08
- Publication Date
- 2026-07-10
AI Technical Summary
Technical Problem
Existing text data augmentation methods often result in generated text that deviates from the original text, affecting model training performance.
Method used
By employing the diffusion and de-diffusion processes of a text diffusion model, the text corpus is converted into text vectors and noise is added. The noise is then predicted and removed through the de-diffusion process, resulting in the restored text vectors, which are then converted into data-augmented text.
Benefits of technology
This solves the problem of text deviating from the original text in traditional methods, ensuring that the data-augmented text conforms to the distribution of the original text, and improving the model training effect.
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Figure CN116108810B_ABST
Abstract
The present disclosure relates to the technical field of text processing, and provides a text data enhancement method and device. The method comprises: obtaining a text corpus set, wherein the text corpus set comprises a plurality of text corpora; converting each text corpus into a text vector by using a diffusion process of a text diffusion model, and obtaining a first target noise vector corresponding to each text corpus by adding noise to each text vector for a plurality of times in succession; predicting a plurality of noises added in the diffusion process by using an inverse diffusion process of the text diffusion model, and removing the plurality of predicted noises in turn by using the first target noise vector corresponding to each text corpus, to obtain a restored text vector corresponding to each text corpus; and converting the restored text vector corresponding to each text corpus into a text, to obtain a first data enhancement text corresponding to each text corpus. By using the above technical means, the problem that the text obtained by a traditional data enhancement method deviates from the original text is solved.
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