SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic

Ibrahim Abu Farha, Silviu Vlad Oprea, Steven Wilson, Walid Magdy


Abstract
iSarcasmEval is the first shared task to target intended sarcasm detection: the data for this task was provided and labelled by the authors of the texts themselves. Such an approach minimises the downfalls of other methods to collect sarcasm data, which rely on distant supervision or third-party annotations. The shared task contains two languages, English and Arabic, and three subtasks: sarcasm detection, sarcasm category classification, and pairwise sarcasm identification given a sarcastic sentence and its non-sarcastic rephrase. The task received submissions from 60 different teams, with the sarcasm detection task being the most popular. Most of the participating teams utilised pre-trained language models. In this paper, we provide an overview of the task, data, and participating teams.
Anthology ID:
2022.semeval-1.111
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Venues:
NAACL | SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
802–814
Language:
URL:
https://aclanthology.org/2022.semeval-1.111
DOI:
10.18653/v1/2022.semeval-1.111
Bibkey:
Cite (ACL):
Ibrahim Abu Farha, Silviu Vlad Oprea, Steven Wilson, and Walid Magdy. 2022. SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 802–814, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic (Abu Farha et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.111.pdf
Code
 iabufarha/isarcasmeval
Data
iSarcasmEval