@inproceedings{singh-etal-2020-newssweeper,
title = "news{S}weeper at {S}em{E}val-2020 Task 11: Context-Aware Rich Feature Representations for Propaganda Classification",
author = "Singh, Paramansh and
Sandhu, Siraj and
Kumar, Subham and
Modi, Ashutosh",
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-1.231",
doi = "10.18653/v1/2020.semeval-1.231",
pages = "1764--1770",
abstract = "This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles for each of the two subtasks of Span Identification and Technique Classification. We make use of pre-trained BERT language model enhanced with tagging techniques developed for the task of Named Entity Recognition (NER), to develop a system for identifying propaganda spans in the text. For the second subtask, we incorporate contextual features in a pre-trained RoBERTa model for the classification of propaganda techniques. We were ranked 5th in the propaganda technique classification subtask.",
}
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<abstract>This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles for each of the two subtasks of Span Identification and Technique Classification. We make use of pre-trained BERT language model enhanced with tagging techniques developed for the task of Named Entity Recognition (NER), to develop a system for identifying propaganda spans in the text. For the second subtask, we incorporate contextual features in a pre-trained RoBERTa model for the classification of propaganda techniques. We were ranked 5th in the propaganda technique classification subtask.</abstract>
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%0 Conference Proceedings
%T newsSweeper at SemEval-2020 Task 11: Context-Aware Rich Feature Representations for Propaganda Classification
%A Singh, Paramansh
%A Sandhu, Siraj
%A Kumar, Subham
%A Modi, Ashutosh
%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 singh-etal-2020-newssweeper
%X This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles for each of the two subtasks of Span Identification and Technique Classification. We make use of pre-trained BERT language model enhanced with tagging techniques developed for the task of Named Entity Recognition (NER), to develop a system for identifying propaganda spans in the text. For the second subtask, we incorporate contextual features in a pre-trained RoBERTa model for the classification of propaganda techniques. We were ranked 5th in the propaganda technique classification subtask.
%R 10.18653/v1/2020.semeval-1.231
%U https://aclanthology.org/2020.semeval-1.231
%U https://doi.org/10.18653/v1/2020.semeval-1.231
%P 1764-1770
Markdown (Informal)
[newsSweeper at SemEval-2020 Task 11: Context-Aware Rich Feature Representations for Propaganda Classification](https://aclanthology.org/2020.semeval-1.231) (Singh et al., SemEval 2020)
ACL