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

Definition modeling is an important task in advanced natural languageapplications such as understanding and conversation. Since its introduction, itfocus on generating one definition for a target word or phrase in a givencontext, which we refer to as Single Definition Modeling (SDM). However, thisapproach does not adequately model the correlations and patterns amongdifferent contexts and definitions of words. In addition, the creation of atraining dataset for SDM requires significant human expertise and effort. Inthis paper, we carefully design a new task called Multiple Definition Modeling(MDM) that pool together all contexts and definition of target words. Wedemonstrate the ease of creating a model as well as multiple training setsautomatically. % In the experiments, we demonstrate and analyze the benefits ofMDM, including improving SDM's performance by using MDM as the pretraining taskand its comparable performance in the zero-shot setting.

Exploiting Correlations Between Contexts and Definitions with Multiple Definition Modeling