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/

Zendy Plus

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

Zendy Tools

Zendy Open

Natural language understanding is a challenging problem that covers a widerange of tasks. While previous methods generally train each task separately, weconsider combining the cross-task features to enhance the task performance. Inthis paper, we incorporate the logic information with the help of the NaturalLanguage Inference (NLI) task to the Story Cloze Test (SCT). Previous work onSCT considered various semantic information, such as sentiment and topic, butlack the logic information between sentences which is an essential element ofstories. Thus we propose to extract the logic information during the course ofthe story to improve the understanding of the whole story. The logicinformation is modeled with the help of the NLI task. Experimental resultsprove the strength of the logic information.

Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?