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

Recent neural models for data-to-text generation are mostly based ondata-driven end-to-end training over encoder-decoder networks. Even though thegenerated texts are mostly fluent and informative, they often generatedescriptions that are not consistent with the input structured data. This is acritical issue especially in domains that require inference or calculationsover raw data. In this paper, we attempt to improve the fidelity of neuraldata-to-text generation by utilizing pre-executed symbolic operations. Wepropose a framework called Operation-guided Attention-basedsequence-to-sequence network (OpAtt), with a specifically designed gatingmechanism as well as a quantization module for operation results to utilizeinformation from pre-executed operations. Experiments on two sports datasetsshow our proposed method clearly improves the fidelity of the generated textsto the input structured data.

Operations Guided Neural Networks for High Fidelity Data-To-Text Generation