@inproceedings{dimov-etal-2020-nopropaganda,
title = "{N}o{P}ropaganda at {S}em{E}val-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification",
author = "Dimov, Ilya and
Korzun, Vladislav and
Smurov, Ivan",
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-1.194",
doi = "10.18653/v1/2020.semeval-1.194",
pages = "1488--1494",
abstract = "This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to predict long continuous propaganda spans for the first subtask. We also adopt an approach from relation extraction by enveloping spans mentioned above with special tokens for the second subtask of propaganda technique classification. Our models report an F-score of 44.6{\%} and a micro-averaged F-score of 58.2{\%} for those tasks accordingly.",
}
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<abstract>This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to predict long continuous propaganda spans for the first subtask. We also adopt an approach from relation extraction by enveloping spans mentioned above with special tokens for the second subtask of propaganda technique classification. Our models report an F-score of 44.6% and a micro-averaged F-score of 58.2% for those tasks accordingly.</abstract>
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%0 Conference Proceedings
%T NoPropaganda at SemEval-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification
%A Dimov, Ilya
%A Korzun, Vladislav
%A Smurov, Ivan
%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 dimov-etal-2020-nopropaganda
%X This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to predict long continuous propaganda spans for the first subtask. We also adopt an approach from relation extraction by enveloping spans mentioned above with special tokens for the second subtask of propaganda technique classification. Our models report an F-score of 44.6% and a micro-averaged F-score of 58.2% for those tasks accordingly.
%R 10.18653/v1/2020.semeval-1.194
%U https://aclanthology.org/2020.semeval-1.194
%U https://doi.org/10.18653/v1/2020.semeval-1.194
%P 1488-1494
Markdown (Informal)
[NoPropaganda at SemEval-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification](https://aclanthology.org/2020.semeval-1.194) (Dimov et al., SemEval 2020)
ACL