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

The absence of labeled data for training neural models is often addressed byleveraging knowledge about the specific task, resulting in heuristic but noisylabels. The knowledge is captured in labeling functions, which detect certainregularities or patterns in the training samples and annotate correspondinglabels for training. This process of weakly supervised training may result inan over-reliance on the signals captured by the labeling functions and hindermodels to exploit other signals or to generalize well. We propose KnowMAN, anadversarial scheme that enables to control influence of signals associated withspecific labeling functions. KnowMAN forces the network to learnrepresentations that are invariant to those signals and to pick up othersignals that are more generally associated with an output label. KnowMANstrongly improves results compared to direct weakly supervised learning with apre-trained transformer language model and a feature-based baseline.

KnowMAN: Weakly Supervised Multinomial Adversarial Networks