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    <abstract>This paper describes the systematic approach applied in “SemEval-2022 Task 6 (iSarcasmEval) : Intended Sarcasm Detection in English and Arabic”. In particular, we illustrate the proposed system in detail for SubTask-A about determining a given text as sarcastic or non-sarcastic in English. We start with the training data from the officially released data and then experiment with different combinations of public datasets to improve the model generalization. Additional experiments conducted on the task demonstrate our strategies are effective in completing the task. Different transformer-based language models, as well as some popular plug-and-play proirs, are mixed into our system to enhance the model’s robustness. Furthermore, statistical and lexical-based text features are mined to improve the accuracy of the sarcasm detection. Our final submission achieves an F1-score for the sarcastic class of 0.6052 on the official test set (the top 1 of the 43 teams in “SubTask-A-English” on the leaderboard).</abstract>
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%0 Conference Proceedings
%T stce at SemEval-2022 Task 6: Sarcasm Detection in English Tweets
%A Yuan, Mengfei
%A Mengyuan, Zhou
%A Jiang, Lianxin
%A Mo, Yang
%A Shi, Xiaofeng
%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 yuan-etal-2022-stce
%X This paper describes the systematic approach applied in “SemEval-2022 Task 6 (iSarcasmEval) : Intended Sarcasm Detection in English and Arabic”. In particular, we illustrate the proposed system in detail for SubTask-A about determining a given text as sarcastic or non-sarcastic in English. We start with the training data from the officially released data and then experiment with different combinations of public datasets to improve the model generalization. Additional experiments conducted on the task demonstrate our strategies are effective in completing the task. Different transformer-based language models, as well as some popular plug-and-play proirs, are mixed into our system to enhance the model’s robustness. Furthermore, statistical and lexical-based text features are mined to improve the accuracy of the sarcasm detection. Our final submission achieves an F1-score for the sarcastic class of 0.6052 on the official test set (the top 1 of the 43 teams in “SubTask-A-English” on the leaderboard).
%R 10.18653/v1/2022.semeval-1.113
%U https://aclanthology.org/2022.semeval-1.113
%U https://doi.org/10.18653/v1/2022.semeval-1.113
%P 820-826

Markdown (Informal)

[stce at SemEval-2022 Task 6: Sarcasm Detection in English Tweets](https://aclanthology.org/2022.semeval-1.113) (Yuan et al., SemEval 2022)

• stce at SemEval-2022 Task 6: Sarcasm Detection in English Tweets (Yuan et al., SemEval 2022)

ACL

• Mengfei Yuan, Zhou Mengyuan, Lianxin Jiang, Yang Mo, and Xiaofeng Shi. 2022. stce at SemEval-2022 Task 6: Sarcasm Detection in English Tweets. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 820–826, Seattle, United States. Association for Computational Linguistics.