@inproceedings{chernyavskiy-etal-2024-unleashing,
title = "Unleashing the Power of Discourse-Enhanced Transformers for Propaganda Detection",
author = "Chernyavskiy, Alexander and
Ilvovsky, Dmitry and
Nakov, Preslav",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.87",
pages = "1452--1462",
abstract = "The prevalence of information manipulation online has created a need for propaganda detection systems. Such systems have typically focused on the surface words, ignoring the linguistic structure. Here we aim to bridge this gap. In particular, we present the first attempt at using discourse analysis for the task. We consider both paragraph-level and token-level classification and we propose a discourse-aware Transformer architecture. Our experiments on English and Russian demonstrate sizeable performance gains compared to a number of baselines. Moreover, our ablation study emphasizes the importance of specific types of discourse features, and our in-depth analysis reveals a strong correlation between propaganda instances and discourse spans.",
}
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%0 Conference Proceedings
%T Unleashing the Power of Discourse-Enhanced Transformers for Propaganda Detection
%A Chernyavskiy, Alexander
%A Ilvovsky, Dmitry
%A Nakov, Preslav
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F chernyavskiy-etal-2024-unleashing
%X The prevalence of information manipulation online has created a need for propaganda detection systems. Such systems have typically focused on the surface words, ignoring the linguistic structure. Here we aim to bridge this gap. In particular, we present the first attempt at using discourse analysis for the task. We consider both paragraph-level and token-level classification and we propose a discourse-aware Transformer architecture. Our experiments on English and Russian demonstrate sizeable performance gains compared to a number of baselines. Moreover, our ablation study emphasizes the importance of specific types of discourse features, and our in-depth analysis reveals a strong correlation between propaganda instances and discourse spans.
%U https://aclanthology.org/2024.eacl-long.87
%P 1452-1462
Markdown (Informal)
[Unleashing the Power of Discourse-Enhanced Transformers for Propaganda Detection](https://aclanthology.org/2024.eacl-long.87) (Chernyavskiy et al., EACL 2024)
ACL
