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 Contextual Query Rewrite (CQR) a dataset for multi-domaintask-oriented spoken dialogue systems that is an extension of the Stanforddialog corpus (Eric et al., 2017a). While previous approaches have addressedthe issue of diverse schemas by learning candidate transformations (Naik etal., 2018), we instead model the reference resolution task as a user queryreformulation task, where the dialog state is serialized into a naturallanguage query that can be executed by the downstream spoken languageunderstanding system. In this paper, we describe our methodology for creatingthe query reformulation extension to the dialog corpus, and present an initialset of experiments to establish a baseline for the CQR task. We have releasedthe corpus to the public [1] to support further research in this area.

A dataset for resolving referring expressions in spoken dialogue via contextual query rewrites (CQR)