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 present a multi-modal dialogue system for interactive learning ofperceptually grounded word meanings from a human tutor. The system integratesan incremental, semantic parsing/generation framework - Dynamic Syntax and TypeTheory with Records (DS-TTR) - with a set of visual classifiers that arelearned throughout the interaction and which ground the meaning representationsthat it produces. We use this system in interaction with a simulated humantutor to study the effects of different dialogue policies and capabilities onthe accuracy of learned meanings, learning rates, and efforts/costs to thetutor. We show that the overall performance of the learning agent is affectedby (1) who takes initiative in the dialogues; (2) the ability to express/usetheir confidence level about visual attributes; and (3) the ability to processelliptical and incrementally constructed dialogue turns. Ultimately, we trainan adaptive dialogue policy which optimises the trade-off between classifieraccuracy and tutoring costs.

Training an adaptive dialogue policy for interactive learning of visually grounded word meanings