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.

本文研究了文本挖掘中的讽刺检测，特别关注作者意图和读者解读的区别，介绍了作者直接标注的 iSarcasm 数据集，并指出现有方法在捕捉作者意图上的局限性，提出未来自然语言处理研究需要发展更优秀的检测方法。