@inproceedings{raj-etal-2020-solomon,
title = "{S}olomon at {S}em{E}val-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles",
author = "Raj, Mayank and
Jaiswal, Ajay and
R.R, Rohit and
Gupta, Ankita and
Sahoo, Sudeep Kumar and
Srivastava, Vertika and
Kim, Yeon Hyang",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.236",
doi = "10.18653/v1/2020.semeval-1.236",
pages = "1802--1807",
abstract = "This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 {''}Detection of Propaganda Techniques in News Articles{''}. We participated in Task {''}Technique Classification{''} (TC) which is a multi-class classification task. To address the TC task, we used RoBERTa based transformer architecture for fine-tuning on the propaganda dataset. The predictions of RoBERTa were further fine-tuned by class-dependent-minority-class classifiers. A special classifier, which employs dynamically adapted Least Common Sub-sequence algorithm, is used to adapt to the intricacies of repetition class. Compared to the other participating systems, our submission is ranked 4th on the leaderboard.",
}
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<abstract>This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 ”Detection of Propaganda Techniques in News Articles”. We participated in Task ”Technique Classification” (TC) which is a multi-class classification task. To address the TC task, we used RoBERTa based transformer architecture for fine-tuning on the propaganda dataset. The predictions of RoBERTa were further fine-tuned by class-dependent-minority-class classifiers. A special classifier, which employs dynamically adapted Least Common Sub-sequence algorithm, is used to adapt to the intricacies of repetition class. Compared to the other participating systems, our submission is ranked 4th on the leaderboard.</abstract>
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%0 Conference Proceedings
%T Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles
%A Raj, Mayank
%A Jaiswal, Ajay
%A R.R, Rohit
%A Gupta, Ankita
%A Sahoo, Sudeep Kumar
%A Srivastava, Vertika
%A Kim, Yeon Hyang
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F raj-etal-2020-solomon
%X This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 ”Detection of Propaganda Techniques in News Articles”. We participated in Task ”Technique Classification” (TC) which is a multi-class classification task. To address the TC task, we used RoBERTa based transformer architecture for fine-tuning on the propaganda dataset. The predictions of RoBERTa were further fine-tuned by class-dependent-minority-class classifiers. A special classifier, which employs dynamically adapted Least Common Sub-sequence algorithm, is used to adapt to the intricacies of repetition class. Compared to the other participating systems, our submission is ranked 4th on the leaderboard.
%R 10.18653/v1/2020.semeval-1.236
%U https://aclanthology.org/2020.semeval-1.236
%U https://doi.org/10.18653/v1/2020.semeval-1.236
%P 1802-1807
Markdown (Informal)
[Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles](https://aclanthology.org/2020.semeval-1.236) (Raj et al., SemEval 2020)
ACL