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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Despite significant progress in text generation models, a serious limitationis their tendency to produce text that is factually inconsistent withinformation in the input. Recent work has studied whether textual entailmentsystems can be used to identify factual errors; however, these sentence-levelentailment models are trained to solve a different problem than generationfiltering and they do not localize which part of a generation is non-factual.In this paper, we propose a new formulation of entailment that decomposes it atthe level of dependency arcs. Rather than focusing on aggregate decisions, weinstead ask whether the semantic relationship manifested by individualdependency arcs in the generated output is supported by the input. Humanjudgments on this task are difficult to obtain; we therefore propose a methodto automatically create data based on existing entailment or paraphrasecorpora. Experiments show that our dependency arc entailment model trained onthis data can identify factual inconsistencies in paraphrasing andsummarization better than sentence-level methods or those based on questiongeneration, while additionally localizing the erroneous parts of thegeneration.

Evaluating Factuality in Generation with Dependency-level Entailment