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

Despite progress in automated fact-checking, most systems require asignificant amount of labeled training data, which is expensive. In this paper,we propose a novel zero-shot method, which instead of operating directly on theclaim and evidence sentences, decomposes them into semantic triples augmentedusing external knowledge graphs, and uses large language models trained fornatural language inference. This allows it to generalize to adversarialdatasets and domains that supervised models require specific training data for.Our empirical results show that our approach outperforms previous zero-shotapproaches on FEVER, FEVER-Symmetric, FEVER 2.0, and Climate-FEVER, while beingcomparable or better than supervised models on the adversarial and theout-of-domain datasets.

Zero-Shot Fact-Checking with Semantic Triples and Knowledge Graphs