@inproceedings{abu-farha-etal-2022-semeval,
title = "{S}em{E}val-2022 Task 6: i{S}arcasm{E}val, Intended Sarcasm Detection in {E}nglish and {A}rabic",
author = "Abu Farha, Ibrahim and
Oprea, Silviu Vlad and
Wilson, Steven and
Magdy, Walid",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.111",
doi = "10.18653/v1/2022.semeval-1.111",
pages = "802--814",
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.",
}
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%0 Conference Proceedings
%T SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic
%A Abu Farha, Ibrahim
%A Oprea, Silviu Vlad
%A Wilson, Steven
%A Magdy, Walid
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F abu-farha-etal-2022-semeval
%X 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.
%R 10.18653/v1/2022.semeval-1.111
%U https://aclanthology.org/2022.semeval-1.111
%U https://doi.org/10.18653/v1/2022.semeval-1.111
%P 802-814
Markdown (Informal)
[SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic](https://aclanthology.org/2022.semeval-1.111) (Abu Farha et al., SemEval 2022)
ACL