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

Pre-training models on vast quantities of unlabeled data has emerged as aneffective approach to improving accuracy on many NLP tasks. On the other hand,traditional machine translation has a long history of leveraging unlabeled datathrough noisy channel modeling. The same idea has recently been shown toachieve strong improvements for neural machine translation. Unfortunately,na\"{i}ve noisy channel modeling with modern sequence to sequence models is upto an order of magnitude slower than alternatives. We address this issue byintroducing efficient approximations to make inference with the noisy channelapproach as fast as strong ensembles while increasing accuracy. We also showthat the noisy channel approach can outperform strong pre-training results byachieving a new state of the art on WMT Romanian-English translation.

Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling