Hierarchical Multi-Instance Multi-Label Learning for Detecting Propaganda Techniques

TL;DR

This paper introduces a hierarchical multi-instance multi-label learning approach using RoBERTa for propaganda technique detection, capturing label dependencies and hierarchical relationships to improve classification accuracy.

Contribution

It proposes a novel hierarchical MIML model that incorporates label dependencies via auxiliary classifiers, enhancing propaganda technique classification performance.

Findings

Achieved 2.47% absolute improvement in micro-F1 score.

Outperformed existing models on shared task leaderboard.

Effective modeling of hierarchical label relationships.

Abstract

Since the introduction of the SemEval 2020 Task 11 (Martino et al., 2020a), several approaches have been proposed in the literature for classifying propaganda based on the rhetorical techniques used to influence readers. These methods, however, classify one span at a time, ignoring dependencies from the labels of other spans within the same context. In this paper, we approach propaganda technique classification as a Multi-Instance Multi-Label (MIML) learning problem (Zhou et al., 2012) and propose a simple RoBERTa-based model (Zhuang et al., 2021) for classifying all spans in an article simultaneously. Further, we note that, due to the annotation process where annotators classified the spans by following a decision tree, there is an inherent hierarchical relationship among the different techniques, which existing approaches ignore. We incorporate these hierarchical label dependencies by adding an auxiliary classifier for each node in the decision tree to the training objective and ensembling the predictions from the original and auxiliary classifiers at test time. Overall, our model leads to an absolute improvement of 2.47% micro-F1 over the model from the shared task winning team in a cross-validation setup and is the best performing non-ensemble model on the shared task leaderboard.