@inproceedings{liu-etal-2023-nlubot101,
title = "{NLUB}ot101 at {S}em{E}val-2023 Task 3: An Augmented Multilingual {NLI} Approach Towards Online News Persuasion Techniques Detection",
author = "Liu, Genglin and
Fung, Yi and
Ji, Heng",
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-1.227",
doi = "10.18653/v1/2023.semeval-1.227",
pages = "1636--1643",
abstract = "We describe our submission to SemEval 2023 Task 3, specifically the subtask on persuasion technique detection. In this work, our team NLUBot101 tackled a novel task of classifying persuasion techniques in online news articles at a paragraph level. The low-resource multilingual datasets, along with the imbalanced label distribution, make this task challenging. Our team presented a cross-lingual data augmentation approach and leveraged a recently proposed multilingual natural language inference model to address these challenges. Our solution achieves the highest macro-F1 score for the English task, and top 5 micro-F1 scores on both the English and Russian leaderboards.",
}
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<abstract>We describe our submission to SemEval 2023 Task 3, specifically the subtask on persuasion technique detection. In this work, our team NLUBot101 tackled a novel task of classifying persuasion techniques in online news articles at a paragraph level. The low-resource multilingual datasets, along with the imbalanced label distribution, make this task challenging. Our team presented a cross-lingual data augmentation approach and leveraged a recently proposed multilingual natural language inference model to address these challenges. Our solution achieves the highest macro-F1 score for the English task, and top 5 micro-F1 scores on both the English and Russian leaderboards.</abstract>
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%0 Conference Proceedings
%T NLUBot101 at SemEval-2023 Task 3: An Augmented Multilingual NLI Approach Towards Online News Persuasion Techniques Detection
%A Liu, Genglin
%A Fung, Yi
%A Ji, Heng
%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 liu-etal-2023-nlubot101
%X We describe our submission to SemEval 2023 Task 3, specifically the subtask on persuasion technique detection. In this work, our team NLUBot101 tackled a novel task of classifying persuasion techniques in online news articles at a paragraph level. The low-resource multilingual datasets, along with the imbalanced label distribution, make this task challenging. Our team presented a cross-lingual data augmentation approach and leveraged a recently proposed multilingual natural language inference model to address these challenges. Our solution achieves the highest macro-F1 score for the English task, and top 5 micro-F1 scores on both the English and Russian leaderboards.
%R 10.18653/v1/2023.semeval-1.227
%U https://aclanthology.org/2023.semeval-1.227
%U https://doi.org/10.18653/v1/2023.semeval-1.227
%P 1636-1643
Markdown (Informal)
[NLUBot101 at SemEval-2023 Task 3: An Augmented Multilingual NLI Approach Towards Online News Persuasion Techniques Detection](https://aclanthology.org/2023.semeval-1.227) (Liu et al., SemEval 2023)
ACL