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Estimating Confidence of Predictions of Individual Classifiers and TheirEnsembles for the Genre Classification Task
Genre identification is a kind of non-topic text classification.
Estimating Confidence of Predictions of Individual Classifiers and Their Ensembles for the Genre Classification Task
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 improve our confidence that our classifier made the right decision.
Experiments with adversarial attacks on text genres
Neural models based on pre-trained transformers, such as BERT or XLM-RoBERTa, demonstrate SOTA results in many NLP tasks, including non-topical classification, such as genre identification.
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