@inproceedings{abdel-salam-2022-reamtchka,
title = "reamtchka at {S}em{E}val-2022 Task 6: Investigating the effect of different loss functions for Sarcasm detection for unbalanced datasets",
author = "Abdel-Salam, Reem",
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-1.126",
doi = "10.18653/v1/2022.semeval-1.126",
pages = "896--906",
abstract = "This paper describes the system used in SemEval-2022 Task 6: Intended Sarcasm Detection in English and Arabic. Achieving 20th,3rd places with 34{\&} 47 F1-Sarcastic score for task A, 16th place for task B with 0.0560 F1-macro score, and 10, 6th places for task C with72{\%} and 80{\%} accuracy on the leaderboard. A voting classifier between either multiple different BERT-based models or machine learningmodels is proposed, as our final model. Multiple key points has been extensively examined to overcome the problem of the unbalance ofthe dataset as: type of models, suitable architecture, augmentation, loss function, etc. In addition to that, we present an analysis of ourresults in this work, highlighting its strengths and shortcomings.",
}
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<abstract>This paper describes the system used in SemEval-2022 Task 6: Intended Sarcasm Detection in English and Arabic. Achieving 20th,3rd places with 34& 47 F1-Sarcastic score for task A, 16th place for task B with 0.0560 F1-macro score, and 10, 6th places for task C with72% and 80% accuracy on the leaderboard. A voting classifier between either multiple different BERT-based models or machine learningmodels is proposed, as our final model. Multiple key points has been extensively examined to overcome the problem of the unbalance ofthe dataset as: type of models, suitable architecture, augmentation, loss function, etc. In addition to that, we present an analysis of ourresults in this work, highlighting its strengths and shortcomings.</abstract>
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%0 Conference Proceedings
%T reamtchka at SemEval-2022 Task 6: Investigating the effect of different loss functions for Sarcasm detection for unbalanced datasets
%A Abdel-Salam, Reem
%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 abdel-salam-2022-reamtchka
%X This paper describes the system used in SemEval-2022 Task 6: Intended Sarcasm Detection in English and Arabic. Achieving 20th,3rd places with 34& 47 F1-Sarcastic score for task A, 16th place for task B with 0.0560 F1-macro score, and 10, 6th places for task C with72% and 80% accuracy on the leaderboard. A voting classifier between either multiple different BERT-based models or machine learningmodels is proposed, as our final model. Multiple key points has been extensively examined to overcome the problem of the unbalance ofthe dataset as: type of models, suitable architecture, augmentation, loss function, etc. In addition to that, we present an analysis of ourresults in this work, highlighting its strengths and shortcomings.
%R 10.18653/v1/2022.semeval-1.126
%U https://aclanthology.org/2022.semeval-1.126
%U https://doi.org/10.18653/v1/2022.semeval-1.126
%P 896-906
Markdown (Informal)
[reamtchka at SemEval-2022 Task 6: Investigating the effect of different loss functions for Sarcasm detection for unbalanced datasets](https://aclanthology.org/2022.semeval-1.126) (Abdel-Salam, SemEval 2022)
ACL