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 describe a prototype dialogue response generation model for the customerservice domain at Amazon. The model, which is trained in a weakly supervisedfashion, measures the similarity between customer questions and agent answersusing a dual encoder network, a Siamese-like neural network architecture.Answer templates are extracted from embeddings derived from past agent answers,without turn-by-turn annotations. Responses to customer inquiries are generatedby selecting the best template from the final set of templates. We show that,in a closed domain like customer service, the selected templates cover $>$70\%of past customer inquiries. Furthermore, the relevance of the model-selectedtemplates is significantly higher than templates selected by a standard tf-idfbaseline.

A practical approach to dialogue response generation in closed domains