@inproceedings{ma-etal-2023-pai,
title = "{PAI} at {S}em{E}val-2023 Task 4: A General Multi-label Classification System with Class-balanced Loss Function and Ensemble Module",
author = "Ma, Long and
Sun, Zeye and
Jiang, Jiawei and
Li, Xuan",
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-1.34",
doi = "10.18653/v1/2023.semeval-1.34",
pages = "256--261",
abstract = "The Human Value Detection shared task{\textbackslash}cite{kiesel:2023} aims to classify whether or not the argument draws on a set of 20 value categories, given a textual argument. This is a difficult task as the discrimination of human values behind arguments is often implicit. Moreover, the number of label categories can be up to 20 and the distribution of data is highly imbalanced. To address these issues, we employ a multi-label classification model and utilize a class-balanced loss function. Our system wins 5 first places, 2 second places, and 6 third places out of 20 categories of the Human Value Detection shared task, and our overall average score of 0.54 also places third. The code is publicly available at {\textbackslash}url{https://www.github.com/diqiuzhuanzhuan/semeval2023}.",
}
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<abstract>The Human Value Detection shared task\textbackslashcitekiesel:2023 aims to classify whether or not the argument draws on a set of 20 value categories, given a textual argument. This is a difficult task as the discrimination of human values behind arguments is often implicit. Moreover, the number of label categories can be up to 20 and the distribution of data is highly imbalanced. To address these issues, we employ a multi-label classification model and utilize a class-balanced loss function. Our system wins 5 first places, 2 second places, and 6 third places out of 20 categories of the Human Value Detection shared task, and our overall average score of 0.54 also places third. The code is publicly available at \textbackslashurlhttps://www.github.com/diqiuzhuanzhuan/semeval2023.</abstract>
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%0 Conference Proceedings
%T PAI at SemEval-2023 Task 4: A General Multi-label Classification System with Class-balanced Loss Function and Ensemble Module
%A Ma, Long
%A Sun, Zeye
%A Jiang, Jiawei
%A Li, Xuan
%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 ma-etal-2023-pai
%X The Human Value Detection shared task\textbackslashcitekiesel:2023 aims to classify whether or not the argument draws on a set of 20 value categories, given a textual argument. This is a difficult task as the discrimination of human values behind arguments is often implicit. Moreover, the number of label categories can be up to 20 and the distribution of data is highly imbalanced. To address these issues, we employ a multi-label classification model and utilize a class-balanced loss function. Our system wins 5 first places, 2 second places, and 6 third places out of 20 categories of the Human Value Detection shared task, and our overall average score of 0.54 also places third. The code is publicly available at \textbackslashurlhttps://www.github.com/diqiuzhuanzhuan/semeval2023.
%R 10.18653/v1/2023.semeval-1.34
%U https://aclanthology.org/2023.semeval-1.34
%U https://doi.org/10.18653/v1/2023.semeval-1.34
%P 256-261
Markdown (Informal)
[PAI at SemEval-2023 Task 4: A General Multi-label Classification System with Class-balanced Loss Function and Ensemble Module](https://aclanthology.org/2023.semeval-1.34) (Ma et al., SemEval 2023)
ACL