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

Autoregressive decoding is the only part of sequence-to-sequence models thatprevents them from massive parallelization at inference time.Non-autoregressive models enable the decoder to generate all output symbolsindependently in parallel. We present a novel non-autoregressive architecturebased on connectionist temporal classification and evaluate it on the task ofneural machine translation. Unlike other non-autoregressive methods whichoperate in several steps, our model can be trained end-to-end. We conductexperiments on the WMT English-Romanian and English-German datasets. Our modelsachieve a significant speedup over the autoregressive models, keeping thetranslation quality comparable to other non-autoregressive models.

End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification