@inproceedings{alhindi-etal-2019-fine,
title = "Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels",
author = "Alhindi, Tariq and
Pfeiffer, Jonas and
Muresan, Smaranda",
editor = "Feldman, Anna and
Da San Martino, Giovanni and
Barr{\'o}n-Cede{\~n}o, Alberto and
Brew, Chris and
Leberknight, Chris and
Nakov, Preslav",
booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5013",
doi = "10.18653/v1/D19-5013",
pages = "98--102",
abstract = "This paper presents the CUNLP submission for the NLP4IF 2019 shared-task on Fine-Grained Propaganda Detection. Our system finished 5th out of 26 teams on the sentence-level classification task and 5th out of 11 teams on the fragment-level classification task based on our scores on the blind test set. We present our models, a discussion of our ablation studies and experiments, and an analysis of our performance on all eighteen propaganda techniques present in the corpus of the shared task.",
}
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<abstract>This paper presents the CUNLP submission for the NLP4IF 2019 shared-task on Fine-Grained Propaganda Detection. Our system finished 5th out of 26 teams on the sentence-level classification task and 5th out of 11 teams on the fragment-level classification task based on our scores on the blind test set. We present our models, a discussion of our ablation studies and experiments, and an analysis of our performance on all eighteen propaganda techniques present in the corpus of the shared task.</abstract>
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%0 Conference Proceedings
%T Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels
%A Alhindi, Tariq
%A Pfeiffer, Jonas
%A Muresan, Smaranda
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Barrón-Cedeño, Alberto
%Y Brew, Chris
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F alhindi-etal-2019-fine
%X This paper presents the CUNLP submission for the NLP4IF 2019 shared-task on Fine-Grained Propaganda Detection. Our system finished 5th out of 26 teams on the sentence-level classification task and 5th out of 11 teams on the fragment-level classification task based on our scores on the blind test set. We present our models, a discussion of our ablation studies and experiments, and an analysis of our performance on all eighteen propaganda techniques present in the corpus of the shared task.
%R 10.18653/v1/D19-5013
%U https://aclanthology.org/D19-5013
%U https://doi.org/10.18653/v1/D19-5013
%P 98-102
Markdown (Informal)
[Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels](https://aclanthology.org/D19-5013) (Alhindi et al., NLP4IF 2019)
ACL