@inproceedings{wang-etal-2020-leveraging,
title = "Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection",
author = "Wang, Ruize and
Tang, Duyu and
Duan, Nan and
Zhong, Wanjun and
Wei, Zhongyu and
Huang, Xuanjing and
Jiang, Daxin and
Zhou, Ming",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.320",
doi = "10.18653/v1/2020.emnlp-main.320",
pages = "3895--3903",
abstract = "We study the detection of propagandistic text fragments in news articles. Instead of merely learning from input-output datapoints in training data, we introduce an approach to inject declarative knowledge of fine-grained propaganda techniques. Specifically, we leverage the declarative knowledge expressed in both first-order logic and natural language. The former refers to the logical consistency between coarse- and fine-grained predictions, which is used to regularize the training process with propositional Boolean expressions. The latter refers to the literal definition of each propaganda technique, which is utilized to get class representations for regularizing the model parameters. We conduct experiments on Propaganda Techniques Corpus, a large manually annotated dataset for fine-grained propaganda detection. Experiments show that our method achieves superior performance, demonstrating that leveraging declarative knowledge can help the model to make more accurate predictions.",
}
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<abstract>We study the detection of propagandistic text fragments in news articles. Instead of merely learning from input-output datapoints in training data, we introduce an approach to inject declarative knowledge of fine-grained propaganda techniques. Specifically, we leverage the declarative knowledge expressed in both first-order logic and natural language. The former refers to the logical consistency between coarse- and fine-grained predictions, which is used to regularize the training process with propositional Boolean expressions. The latter refers to the literal definition of each propaganda technique, which is utilized to get class representations for regularizing the model parameters. We conduct experiments on Propaganda Techniques Corpus, a large manually annotated dataset for fine-grained propaganda detection. Experiments show that our method achieves superior performance, demonstrating that leveraging declarative knowledge can help the model to make more accurate predictions.</abstract>
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%0 Conference Proceedings
%T Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection
%A Wang, Ruize
%A Tang, Duyu
%A Duan, Nan
%A Zhong, Wanjun
%A Wei, Zhongyu
%A Huang, Xuanjing
%A Jiang, Daxin
%A Zhou, Ming
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-leveraging
%X We study the detection of propagandistic text fragments in news articles. Instead of merely learning from input-output datapoints in training data, we introduce an approach to inject declarative knowledge of fine-grained propaganda techniques. Specifically, we leverage the declarative knowledge expressed in both first-order logic and natural language. The former refers to the logical consistency between coarse- and fine-grained predictions, which is used to regularize the training process with propositional Boolean expressions. The latter refers to the literal definition of each propaganda technique, which is utilized to get class representations for regularizing the model parameters. We conduct experiments on Propaganda Techniques Corpus, a large manually annotated dataset for fine-grained propaganda detection. Experiments show that our method achieves superior performance, demonstrating that leveraging declarative knowledge can help the model to make more accurate predictions.
%R 10.18653/v1/2020.emnlp-main.320
%U https://aclanthology.org/2020.emnlp-main.320
%U https://doi.org/10.18653/v1/2020.emnlp-main.320
%P 3895-3903
Markdown (Informal)
[Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection](https://aclanthology.org/2020.emnlp-main.320) (Wang et al., EMNLP 2020)
ACL