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 is a method for choosing the outputs of amachine learning system based not on the output with the highest probability,but the output with the lowest risk (expected error) among multiple candidates.It is a simple but powerful method: for an additional cost at inference time,MBR provides reliable several-point improvements across metrics for a widevariety of tasks without any additional data or training. Despite this, MBR isnot frequently applied in NLP works, and knowledge of the method itself islimited. We first provide an introduction to the method and the recentliterature. We show that several recent methods that do not reference MBR canbe written as special cases of MBR; this reformulation provides additionaltheoretical justification for the performance of these methods, explaining someresults that were previously only empirical. We provide theoretical andempirical results about the effectiveness of various MBR variants and makeconcrete recommendations for the application of MBR in NLP models, includingfuture directions in this area.

It's MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk