We report our participation in the SemEval-2023 shared task on propaganda detection and describe our solutions with pre-trained models and their ensembles. For Subtask 1 (News Genre Categorisation), we report the impact of several settings, such as the choice of the classification models (monolingual or multilingual or their ensembles), the choice of the training sets (base or additional sources), the impact of detection certainty in making a classification decision as well as the impact of other hyper-parameters. In particular, we fine-tune models on additional data for other genre classification tasks, such as FTD. We also try adding texts from genre-homogenous corpora, such as Panorama, Babylon Bee for satire and Giganews for for reporting texts. We also make prepared models for Subtasks 2 and 3 with finetuning the corresponding models first for Subtask 1.The code needed to reproduce the experiments is available.

Genre identification is a kind of non-topic text classification. The main difference between this task and topic classification is that genre, unlike topic, usually cannot be expressed just by some keywords and is defined as a functional space. Neural models based on pre-trained transformers, such as BERT or XLM-RoBERTa, demonstrate SOTA results in many NLP tasks, including non-topical classification. However, in many cases, their downstream application to very large corpora, such as those extracted from social media, can lead to unreliable results because of dataset shifts, when some raw texts do not match the profile of the training set. To mitigate this problem, we experiment with individual models as well as with their ensembles. To evaluate the robustness of all models we use a prediction confidence metric, which estimates the reliability of a prediction in the absence of a gold standard label. We can evaluate robustness via the confidence gap between the correctly classified texts and the misclassified ones on a labeled test corpus, higher gaps make it easier to identify whether a text is classified correctly. Our results show that for all of the classifiers tested in this study, there is a confidence gap, but for the ensembles, the gap is wider, meaning that ensembles are more robust than their individual models.