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

Norms, which are culturally accepted guidelines for behaviours, can beintegrated into conversational models to generate utterances that areappropriate for the socio-cultural context. Existing methods for normrecognition tend to focus only on surface-level features of dialogues and donot take into account the interactions within a conversation. To address thisissue, we propose NormMark, a probabilistic generative Markov model to carrythe latent features throughout a dialogue. These features are captured bydiscrete and continuous latent variables conditioned on the conversationhistory, and improve the model's ability in norm recognition. The model istrainable on weakly annotated data using the variational technique. On adataset with limited norm annotations, we show that our approach achieveshigher F1 score, outperforming current state-of-the-art methods, includingGPT3.

NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery