iSarcasm: A Dataset of Intended Sarcasm

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 limitationsof 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
Original languageEnglish
Title of host publicationProceedings of the 58th Annual Meeting of the Association for Computational Linguistics
PublisherAssociation for Computational Linguistics (ACL)
Number of pages11
ISBN (Print)978-1-952148-25-5
Publication statusPublished - 10 Jul 2020
Event2020 Annual Conference of the Association for Computational Linguistics - Hyatt Regency Seattle, Virtual conference, United States
Duration: 5 Jul 202010 Jul 2020
Conference number: 58


Conference2020 Annual Conference of the Association for Computational Linguistics
Abbreviated titleACL 2020
Country/TerritoryUnited States
CityVirtual conference
Internet address


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