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

Before entering the neural network, a token is generally converted to thecorresponding one-hot representation, which is a discrete distribution of thevocabulary. Smoothed representation is the probability of candidate tokensobtained from a pre-trained masked language model, which can be seen as a moreinformative substitution to the one-hot representation. We propose an efficientdata augmentation method, termed text smoothing, by converting a sentence fromits one-hot representation to a controllable smoothed representation. Weevaluate text smoothing on different benchmarks in a low-resource regime.Experimental results show that text smoothing outperforms various mainstreamdata augmentation methods by a substantial margin. Moreover, text smoothing canbe combined with those data augmentation methods to achieve better performance.

Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks