@inproceedings{he-zhang-2023-zhegu,
title = "Zhegu at {S}em{E}val-2023 Task 9: Exponential Penalty Mean Squared Loss for Multilingual Tweet Intimacy Analysis",
author = "He, Pan and
Zhang, Yanru",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.43",
doi = "10.18653/v1/2023.semeval-1.43",
pages = "318--323",
abstract = "We present the system description of our team Zhegu in SemEval-2023 Task 9 Multilingual Tweet Intimacy Analysis. We propose {\textbackslash}textbf{EPM} ({\textbackslash}textbf{E}xponential {\textbackslash}textbf{P}enalty {\textbackslash}textbf{M}ean Squared Loss) for the purpose of enhancing the ability of learning difficult samples during the training process. Meanwhile, we also apply several methods (frozen Tuning {\textbackslash}{\&}amp; contrastive learning based on Language) on the XLM-R multilingual language model for fine-tuning and model ensemble. The results in our experiments provide strong faithful evidence of the effectiveness of our methods. Eventually, we achieved a Pearson score of 0.567 on the test set.",
}
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<abstract>We present the system description of our team Zhegu in SemEval-2023 Task 9 Multilingual Tweet Intimacy Analysis. We propose \textbackslashtextbfEPM (\textbackslashtextbfExponential \textbackslashtextbfPenalty \textbackslashtextbfMean Squared Loss) for the purpose of enhancing the ability of learning difficult samples during the training process. Meanwhile, we also apply several methods (frozen Tuning \textbackslash&amp; contrastive learning based on Language) on the XLM-R multilingual language model for fine-tuning and model ensemble. The results in our experiments provide strong faithful evidence of the effectiveness of our methods. Eventually, we achieved a Pearson score of 0.567 on the test set.</abstract>
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%0 Conference Proceedings
%T Zhegu at SemEval-2023 Task 9: Exponential Penalty Mean Squared Loss for Multilingual Tweet Intimacy Analysis
%A He, Pan
%A Zhang, Yanru
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F he-zhang-2023-zhegu
%X We present the system description of our team Zhegu in SemEval-2023 Task 9 Multilingual Tweet Intimacy Analysis. We propose \textbackslashtextbfEPM (\textbackslashtextbfExponential \textbackslashtextbfPenalty \textbackslashtextbfMean Squared Loss) for the purpose of enhancing the ability of learning difficult samples during the training process. Meanwhile, we also apply several methods (frozen Tuning \textbackslash& contrastive learning based on Language) on the XLM-R multilingual language model for fine-tuning and model ensemble. The results in our experiments provide strong faithful evidence of the effectiveness of our methods. Eventually, we achieved a Pearson score of 0.567 on the test set.
%R 10.18653/v1/2023.semeval-1.43
%U https://aclanthology.org/2023.semeval-1.43
%U https://doi.org/10.18653/v1/2023.semeval-1.43
%P 318-323
Markdown (Informal)
[Zhegu at SemEval-2023 Task 9: Exponential Penalty Mean Squared Loss for Multilingual Tweet Intimacy Analysis](https://aclanthology.org/2023.semeval-1.43) (He & Zhang, SemEval 2023)
ACL