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

Standard autoregressive seq2seq models are easily trained by max-likelihood,but tend to show poor results under small-data conditions. We introduce a classof seq2seq models, GAMs (Global Autoregressive Models), which combine anautoregressive component with a log-linear component, allowing the use ofglobal \textit{a priori} features to compensate for lack of data. We trainthese models in two steps. In the first step, we obtain an \emph{unnormalized}GAM that maximizes the likelihood of the data, but is improper for fastinference or evaluation. In the second step, we use this GAM to train (bydistillation) a second autoregressive model that approximates the\emph{normalized} distribution associated with the GAM, and can be used forfast inference and evaluation. Our experiments focus on language modellingunder synthetic conditions and show a strong perplexity reduction of using thesecond autoregressive model over the standard one.

Global Autoregressive Models for Data-Efficient Sequence Learning