@inproceedings{wu-etal-2023-sheffieldveraai,
title = "{S}heffield{V}era{AI} at {S}em{E}val-2023 Task 3: Mono and Multilingual Approaches for News Genre, Topic and Persuasion Technique Classification",
author = "Wu, Ben and
Razuvayevskaya, Olesya and
Heppell, Freddy and
Leite, Jo{\~a}o A. and
Scarton, Carolina and
Bontcheva, Kalina and
Song, Xingyi",
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-1.275",
doi = "10.18653/v1/2023.semeval-1.275",
pages = "1995--2008",
abstract = "This paper describes our approach for SemEval- 2023 Task 3: Detecting the category, the fram- ing, and the persuasion techniques in online news in a multilingual setup. For Subtask 1 (News Genre), we propose an ensemble of fully trained and adapter mBERT models which was ranked joint-first for German, and had the high- est mean rank of multi-language teams. For Subtask 2 (Framing), we achieved first place in 3 languages, and the best average rank across all the languages, by using two separate ensem- bles: a monolingual RoBERTa-MUPPETLARGE and an ensemble of XLM-RoBERTaLARGE with adapters and task adaptive pretraining. For Sub- task 3 (Persuasion Techniques), we trained a monolingual RoBERTa-Base model for English and a multilingual mBERT model for the re- maining languages, which achieved top 10 for all languages, including 2nd for English. For each subtask, we compared monolingual and multilingual approaches, and considered class imbalance techniques.",
}
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<abstract>This paper describes our approach for SemEval- 2023 Task 3: Detecting the category, the fram- ing, and the persuasion techniques in online news in a multilingual setup. For Subtask 1 (News Genre), we propose an ensemble of fully trained and adapter mBERT models which was ranked joint-first for German, and had the high- est mean rank of multi-language teams. For Subtask 2 (Framing), we achieved first place in 3 languages, and the best average rank across all the languages, by using two separate ensem- bles: a monolingual RoBERTa-MUPPETLARGE and an ensemble of XLM-RoBERTaLARGE with adapters and task adaptive pretraining. For Sub- task 3 (Persuasion Techniques), we trained a monolingual RoBERTa-Base model for English and a multilingual mBERT model for the re- maining languages, which achieved top 10 for all languages, including 2nd for English. For each subtask, we compared monolingual and multilingual approaches, and considered class imbalance techniques.</abstract>
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%0 Conference Proceedings
%T SheffieldVeraAI at SemEval-2023 Task 3: Mono and Multilingual Approaches for News Genre, Topic and Persuasion Technique Classification
%A Wu, Ben
%A Razuvayevskaya, Olesya
%A Heppell, Freddy
%A Leite, João A.
%A Scarton, Carolina
%A Bontcheva, Kalina
%A Song, Xingyi
%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 wu-etal-2023-sheffieldveraai
%X This paper describes our approach for SemEval- 2023 Task 3: Detecting the category, the fram- ing, and the persuasion techniques in online news in a multilingual setup. For Subtask 1 (News Genre), we propose an ensemble of fully trained and adapter mBERT models which was ranked joint-first for German, and had the high- est mean rank of multi-language teams. For Subtask 2 (Framing), we achieved first place in 3 languages, and the best average rank across all the languages, by using two separate ensem- bles: a monolingual RoBERTa-MUPPETLARGE and an ensemble of XLM-RoBERTaLARGE with adapters and task adaptive pretraining. For Sub- task 3 (Persuasion Techniques), we trained a monolingual RoBERTa-Base model for English and a multilingual mBERT model for the re- maining languages, which achieved top 10 for all languages, including 2nd for English. For each subtask, we compared monolingual and multilingual approaches, and considered class imbalance techniques.
%R 10.18653/v1/2023.semeval-1.275
%U https://aclanthology.org/2023.semeval-1.275
%U https://doi.org/10.18653/v1/2023.semeval-1.275
%P 1995-2008
Markdown (Informal)
[SheffieldVeraAI at SemEval-2023 Task 3: Mono and Multilingual Approaches for News Genre, Topic and Persuasion Technique Classification](https://aclanthology.org/2023.semeval-1.275) (Wu et al., SemEval 2023)
ACL
- Ben Wu, Olesya Razuvayevskaya, Freddy Heppell, João A. Leite, Carolina Scarton, Kalina Bontcheva, and Xingyi Song. 2023. SheffieldVeraAI at SemEval-2023 Task 3: Mono and Multilingual Approaches for News Genre, Topic and Persuasion Technique Classification. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1995–2008, Toronto, Canada. Association for Computational Linguistics.