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

We introduce neural particle smoothing, a sequential Monte Carlo method forsampling annotations of an input string from a given probability model. Incontrast to conventional particle filtering algorithms, we train a proposaldistribution that looks ahead to the end of the input string by means of aright-to-left LSTM. We demonstrate that this innovation can improve the qualityof the sample. To motivate our formal choices, we explain how our neural modeland neural sampler can be viewed as low-dimensional but nonlinearapproximations to working with HMMs over very large state spaces.

Neural Particle Smoothing for Sampling from Conditional Sequence Models