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

Task-oriented dialog systems are becoming pervasive, and many companiesheavily rely on them to complement human agents for customer service in callcenters. With globalization, the need for providing cross-lingual customersupport becomes more urgent than ever. However, cross-lingual support posesgreat challenges---it requires a large amount of additional annotated data fromnative speakers. In order to bypass the expensive human annotation and achievethe first step towards the ultimate goal of building a universal dialog system,we set out to build a cross-lingual state tracking framework. Specifically, weassume that there exists a source language with dialog belief trackingannotations while the target languages have no annotated dialog data of anyform. Then, we pre-train a state tracker for the source language as a teacher,which is able to exploit easy-to-access parallel data. We then distill andtransfer its own knowledge to the student state tracker in target languages. Wespecifically discuss two types of common parallel resources: bilingual corpusand bilingual dictionary, and design different transfer learning strategiesaccordingly. Experimentally, we successfully use English state tracker as theteacher to transfer its knowledge to both Italian and German trackers andachieve promising results.

XL-NBT: A Cross-lingual Neural Belief Tracking Framework