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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The natural language generation (NLG) component of a spoken dialogue system(SDS) usually needs a substantial amount of handcrafting or a well-labeleddataset to be trained on. These limitations add significantly to developmentcosts and make cross-domain, multi-lingual dialogue systems intractable.Moreover, human languages are context-aware. The most natural response shouldbe directly learned from data rather than depending on predefined syntaxes orrules. This paper presents a statistical language generator based on a jointrecurrent and convolutional neural network structure which can be trained ondialogue act-utterance pairs without any semantic alignments or predefinedgrammar trees. Objective metrics suggest that this new model outperformsprevious methods under the same experimental conditions. Results of anevaluation by human judges indicate that it produces not only high quality butlinguistically varied utterances which are preferred compared to n-gram andrule-based systems.

Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking