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

Many generation tasks follow a one-to-many mapping relationship: each inputcould be associated with multiple outputs. Existing methods like ConditionalVariational AutoEncoder(CVAE) employ a latent variable to model thisone-to-many relationship. However, this high-dimensional and dense latentvariable lacks explainability and usually leads to poor and uncontrollablegenerations. In this paper, we innovatively introduce the linguistic concept ofpattern to decompose the one-to-many mapping into multiple one-to-one mappingsand further propose a model named Sparse Pattern Mixture of Experts(SPMoE).Each one-to-one mapping is associated with a conditional generation pattern andis modeled with an expert in SPMoE. To ensure each language pattern can beexclusively handled with an expert model for better explainability anddiversity, a sparse mechanism is employed to coordinate all the expert modelsin SPMoE. We assess the performance of our SPMoE on the paraphrase generationtask and the experiment results prove that SPMoE can achieve a good balance interms of quality, pattern-level diversity, and corpus-level diversity.

SPMoE: Generate Multiple Pattern-Aware Outputs with Sparse Pattern Mixture of Experts