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

In recent years, Recurrent Neural Networks (RNNs) based models have beenapplied to the Slot Filling problem of Spoken Language Understanding andachieved the state-of-the-art performances. In this paper, we investigate theeffect of incorporating pre-trained language models into RNN based Slot Fillingmodels. Our evaluation on the Airline Travel Information System (ATIS) datacorpus shows that we can significantly reduce the size of labeled training dataand achieve the same level of Slot Filling performance by incorporating extraword embedding and language model embedding layers pre-trained on unlabeledcorpora.

Recurrent Neural Networks with Pre-trained Language Model Embedding for Slot Filling Task