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 propose an architecture to jointly learn word and label embeddings forslot filling in spoken language understanding. The proposed approach encodeslabels using a combination of word embeddings and straightforward word-labelassociation from the training data. Compared to the state-of-the-art methods,our approach does not require label embeddings as part of the input andtherefore lends itself nicely to a wide range of model architectures. Inaddition, our architecture computes contextual distances between words andlabels to avoid adding contextual windows, thus reducing memory footprint. Wevalidate the approach on established spoken dialogue datasets and show that itcan achieve state-of-the-art performance with much fewer trainable parameters.

Joint Learning of Word and Label Embeddings for Sequence Labelling in Spoken Language Understanding