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

Leaderboard systems allow researchers to objectively evaluate NaturalLanguage Processing (NLP) models and are typically used to identify models thatexhibit superior performance on a given task in a predetermined setting.However, we argue that evaluation on a given test dataset is just one of manyperformance indications of the model. In this paper, we claim leaderboardcompetitions should also aim to identify models that exhibit the bestperformance in a real-world setting. We highlight three issues with currentleaderboard systems: (1) the use of a single, static test set, (2) discrepancybetween testing and real-world application (3) the tendency forleaderboard-centric competition to be biased towards the test set. As asolution, we propose a new paradigm of leaderboard systems that addresses theseissues of current leaderboard system. Through this study, we hope to induce aparadigm shift towards more real -world-centric leaderboard competitions.

Self-Improving-Leaderboard(SIL): A Call for Real-World Centric Natural Language Processing Leaderboards