Hierarchical Multi-Instance Multi-Label Learning for Detecting
  Propaganda Techniques
Anni Chen Bhuwan Dhingra
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.