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

Is it possible to build a general and automatic natural language generation(NLG) evaluation metric? Existing learned metrics either performunsatisfactorily or are restricted to tasks where large human rating data isalready available. We introduce SESCORE, a model-based metric that is highlycorrelated with human judgements without requiring human annotation, byutilizing a novel, iterative error synthesis and severity scoring pipeline.This pipeline applies a series of plausible errors to raw text and assignsseverity labels by simulating human judgements with entailment. We evaluateSESCORE against existing metrics by comparing how their scores correlate withhuman ratings. SESCORE outperforms all prior unsupervised metrics on multiplediverse NLG tasks including machine translation, image captioning, and WebNLGtext generation. For WMT 20/21 En-De and Zh-En, SESCORE improve the averageKendall correlation with human judgement from 0.154 to 0.195. SESCORE evenachieves comparable performance to the best supervised metric COMET, despitereceiving no human-annotated training data.

Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis