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

Cross-lingual adaptation has proven effective in spoken languageunderstanding (SLU) systems with limited resources. Existing methods arefrequently unsatisfactory for intent detection and slot filling, particularlyfor distant languages that differ significantly from the source language inscripts, morphology, and syntax. Latent Dialogue Action (LaDA) layer isproposed to optimize decoding strategy in order to address the aforementionedissues. The model consists of an additional layer of latent dialogue action. Itenables our model to improve a system's capability of handling conversationswith complex multilingual intent and slot values of distant languages. To thebest of our knowledge, this is the first exhaustive investigation of the use oflatent variables for optimizing cross-lingual SLU policy during the decodestage. LaDA obtains state-of-the-art results on public datasets for bothzero-shot and few-shot adaptation.

LaDA: Latent Dialogue Action For Zero-shot Cross-lingual Neural Network Language Modeling