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

Minimum Bayes risk (MBR) decoding outputs the hypothesis with the highestexpected utility over the model distribution for some utility function. It hasbeen shown to improve accuracy over beam search in conditional languagegeneration problems and especially neural machine translation, in both humanand automatic evaluations. However, the standard sampling-based algorithm forMBR is substantially more computationally expensive than beam search, requiringa large number of samples as well as a quadratic number of calls to the utilityfunction, limiting its applicability. We describe an algorithm for MBR whichgradually grows the number of samples used to estimate the utility whilepruning hypotheses that are unlikely to have the highest utility according toconfidence estimates obtained with bootstrap sampling. Our method requiresfewer samples and drastically reduces the number of calls to the utilityfunction compared to standard MBR while being statistically indistinguishablein terms of accuracy. We demonstrate the effectiveness of our approach inexperiments on three language pairs, using chrF++ and COMET asutility/evaluation metrics.

Faster Minimum Bayes Risk Decoding with Confidence-based Pruning