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

Most existing neural network based task-oriented dialogue systems followencoder-decoder paradigm, where the decoder purely depends on the source textsto generate a sequence of words, usually suffering from instability and poorreadability. Inspired by the traditional template-based generation approaches,we propose a template-guided hybrid pointer network for the knowledge-basedtask-oriented dialogue system, which retrieves several potentially relevantanswers from a pre-constructed domain-specific conversational repository asguidance answers, and incorporates the guidance answers into both the encodingand decoding processes. Specifically, we design a memory pointer network modelwith a gating mechanism to fully exploit the semantic correlation between theretrieved answers and the ground-truth response. We evaluate our model on fourwidely used task-oriented datasets, including one simulated and three manuallycreated datasets. The experimental results demonstrate that the proposed modelachieves significantly better performance than the state-of-the-art methodsover different automatic evaluation metrics.

A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systems