English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Consistency training regularizes a model by enforcing predictions of originaland perturbed inputs to be similar. Previous studies have proposed variousaugmentation methods for the perturbation but are limited in that they areagnostic to the training model. Thus, the perturbed samples may not aid inregularization due to their ease of classification from the model. In thiscontext, we propose an augmentation method of adding a discrete noise thatwould incur the highest divergence between predictions. This virtualadversarial discrete noise obtained by replacing a small portion of tokenswhile keeping original semantics as much as possible efficiently pushes atraining model's decision boundary. Experimental results show that our proposedmethod outperforms other consistency training baselines with text editing,paraphrasing, or a continuous noise on semi-supervised text classificationtasks and a robustness benchmark

Consistency Training with Virtual Adversarial Discrete Perturbation