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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Recent statistical approaches have improved the robustness and scalability ofspoken dialogue systems. However, despite recent progress in domain adaptation,their reliance on in-domain data still limits their cross-domain scalability.In this paper, we argue that this problem can be addressed by extending currentmodels to reflect and exploit the multi-dimensional nature of human dialogue.We present our multi-dimensional, statistical dialogue management framework, inwhich transferable conversational skills can be learnt by separating outdomain-independent dimensions of communication and using multi-agentreinforcement learning. Our initial experiments with a simulated user show thatwe can speed up the learning process by transferring learnt policies.

Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue Modelling