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

Reference is a crucial property of language that allows us to connectlinguistic expressions to the world. Modeling it requires handling bothcontinuous and discrete aspects of meaning. Data-driven models excel at theformer, but struggle with the latter, and the reverse is true for symbolicmodels.  This paper (a) introduces a concrete referential task to test both aspects,called cross-modal entity tracking; (b) proposes a neural network architecturethat uses external memory to build an entity library inspired in the DRSs ofDRT, with a mechanism to dynamically introduce new referents or add informationto referents that are already in the library.  Our model shows promise: it beats traditional neural network architectures onthe task. However, it is still outperformed by Memory Networks, another modelwith external memory.

Living a discrete life in a continuous world: Reference with distributed representations