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

This paper presents two ways of dealing with scarce data in semantic decodingusing N-Best speech recognition hypotheses. First, we learn features by using adeep learning architecture in which the weights for the unknown and knowncategories are jointly optimised. Second, an unsupervised method is used forfurther tuning the weights. Sharing weights injects prior knowledge to unknowncategories. The unsupervised tuning (i.e. the risk minimisation) improves theF-Measure when recognising nearly zero-shot data on the DSTC3 corpus. Thisunsupervised method can be applied subject to two assumptions: the rank of theclass marginal is assumed to be known and the class-conditional scores of theclassifier are assumed to follow a Gaussian distribution.

Nearly Zero-Shot Learning for Semantic Decoding in Spoken Dialogue Systems