@inproceedings{verma-etal-2020-transformers,
title = "Transformers at {S}em{E}val-2020 Task 11: Propaganda Fragment Detection Using Diversified {BERT} Architectures Based Ensemble Learning",
author = "Verma, Ekansh and
Motupalli, Vinodh and
Chakraborty, Souradip",
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-1.239",
doi = "10.18653/v1/2020.semeval-1.239",
pages = "1823--1828",
abstract = "In this paper, we present our approach for the {'}Detection of Propaganda Techniques in News Articles{'} task as a part of the 2020 edition of International Workshop on Semantic Evaluation. The specific objective of this task is to identify and extract the text segments in which propaganda techniques are used. We propose a multi-system deep learning framework that can be used to identify the presence of propaganda fragments in a news article and also deep dive into the diverse enhancements of BERT architecture which are part of the final solution. Our proposed final model gave an F1-score of 0.48 on the test dataset.",
}
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<abstract>In this paper, we present our approach for the ’Detection of Propaganda Techniques in News Articles’ task as a part of the 2020 edition of International Workshop on Semantic Evaluation. The specific objective of this task is to identify and extract the text segments in which propaganda techniques are used. We propose a multi-system deep learning framework that can be used to identify the presence of propaganda fragments in a news article and also deep dive into the diverse enhancements of BERT architecture which are part of the final solution. Our proposed final model gave an F1-score of 0.48 on the test dataset.</abstract>
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%0 Conference Proceedings
%T Transformers at SemEval-2020 Task 11: Propaganda Fragment Detection Using Diversified BERT Architectures Based Ensemble Learning
%A Verma, Ekansh
%A Motupalli, Vinodh
%A Chakraborty, Souradip
%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 verma-etal-2020-transformers
%X In this paper, we present our approach for the ’Detection of Propaganda Techniques in News Articles’ task as a part of the 2020 edition of International Workshop on Semantic Evaluation. The specific objective of this task is to identify and extract the text segments in which propaganda techniques are used. We propose a multi-system deep learning framework that can be used to identify the presence of propaganda fragments in a news article and also deep dive into the diverse enhancements of BERT architecture which are part of the final solution. Our proposed final model gave an F1-score of 0.48 on the test dataset.
%R 10.18653/v1/2020.semeval-1.239
%U https://aclanthology.org/2020.semeval-1.239
%U https://doi.org/10.18653/v1/2020.semeval-1.239
%P 1823-1828
Markdown (Informal)
[Transformers at SemEval-2020 Task 11: Propaganda Fragment Detection Using Diversified BERT Architectures Based Ensemble Learning](https://aclanthology.org/2020.semeval-1.239) (Verma et al., SemEval 2020)
ACL