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

Many practical applications of dialogue technology require the generation ofresponses according to a particular developer-specified persona. While avariety of personas can be elicited from recent large language models, theopaqueness and unpredictability of these models make it desirable to be able tospecify personas in an explicit form. In previous work, personas have typicallybeen represented as sets of one-off pieces of self-knowledge that are retrievedby the dialogue system for use in generation. However, in realistic humanconversations, personas are often revealed through story-like narratives thatinvolve rich habitual knowledge -- knowledge about kinds of events that anagent often participates in (e.g., work activities, hobbies, sportingactivities, favorite entertainments, etc.), including typical goals,sub-events, preconditions, and postconditions of those events. We capture suchhabitual knowledge using an explicit schema representation, and propose anapproach to dialogue generation that retrieves relevant schemas to condition alarge language model to generate persona-based responses. Furthermore, wedemonstrate a method for bootstrapping the creation of such schemas by firstgenerating generic passages from a set of simple facts, and then inducingschemas from the generated passages.

We are what we repeatedly do: Inducing and deploying habitual schemas in persona-based responses