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 consider the distinction between intended and perceived sarcasm in thecontext of textual sarcasm detection. The former occurs when an utterance issarcastic from the perspective of its author, while the latter occurs when theutterance is interpreted as sarcastic by the audience. We show the limitationsof previous labelling methods in capturing intended sarcasm and introduce theiSarcasm dataset of tweets labeled for sarcasm directly by their authors.Examining the state-of-the-art sarcasm detection models on our dataset showedlow performance compared to previously studied datasets, which indicates thatthese datasets might be biased or obvious and sarcasm could be a phenomenonunder-studied computationally thus far. By providing the iSarcasm dataset, weaim to encourage future NLP research to develop methods for detecting sarcasmin text as intended by the authors of the text, not as labeled underassumptions that we demonstrate to be sub-optimal.

iSarcasm: A Dataset of Intended Sarcasm