@inproceedings{alami-etal-2022-high,
title = "High Tech team at {S}em{E}val-2022 Task 6: Intended Sarcasm Detection for {A}rabic texts",
author = "Alami, Hamza and
Benlahbib, Abdessamad and
Alami, Ahmed",
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-1.116",
doi = "10.18653/v1/2022.semeval-1.116",
pages = "840--843",
abstract = "This paper presents our proposed methods for the iSarcasmEval shared task. The shared task consists of three different subtasks. We participate in both subtask A and subtask C. The purpose of subtask A was to predict if a text is sarcastic while the aim of subtask C is to determine which text is sarcastic given a sarcastic text and its non-sarcastic rephrase. Both of the developed solutions used BERT pre-trained models. The proposed models are optimized on simple objectives and are easy to grasp. However, despite their simplicity, our methods ranked 4 and 2 in iSarcasmEval subtask A and subtask C for Arabic texts.",
}
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<abstract>This paper presents our proposed methods for the iSarcasmEval shared task. The shared task consists of three different subtasks. We participate in both subtask A and subtask C. The purpose of subtask A was to predict if a text is sarcastic while the aim of subtask C is to determine which text is sarcastic given a sarcastic text and its non-sarcastic rephrase. Both of the developed solutions used BERT pre-trained models. The proposed models are optimized on simple objectives and are easy to grasp. However, despite their simplicity, our methods ranked 4 and 2 in iSarcasmEval subtask A and subtask C for Arabic texts.</abstract>
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%0 Conference Proceedings
%T High Tech team at SemEval-2022 Task 6: Intended Sarcasm Detection for Arabic texts
%A Alami, Hamza
%A Benlahbib, Abdessamad
%A Alami, Ahmed
%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 alami-etal-2022-high
%X This paper presents our proposed methods for the iSarcasmEval shared task. The shared task consists of three different subtasks. We participate in both subtask A and subtask C. The purpose of subtask A was to predict if a text is sarcastic while the aim of subtask C is to determine which text is sarcastic given a sarcastic text and its non-sarcastic rephrase. Both of the developed solutions used BERT pre-trained models. The proposed models are optimized on simple objectives and are easy to grasp. However, despite their simplicity, our methods ranked 4 and 2 in iSarcasmEval subtask A and subtask C for Arabic texts.
%R 10.18653/v1/2022.semeval-1.116
%U https://aclanthology.org/2022.semeval-1.116
%U https://doi.org/10.18653/v1/2022.semeval-1.116
%P 840-843
Markdown (Informal)
[High Tech team at SemEval-2022 Task 6: Intended Sarcasm Detection for Arabic texts](https://aclanthology.org/2022.semeval-1.116) (Alami et al., SemEval 2022)
ACL