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

Computation and Language