Papers › Cost-Sensitive BERT for Generalisable Sentence Classification with Imbalanced Data

Cost-Sensitive BERT for Generalisable Sentence Classification with Imbalanced Data

16 Mar 2020arXiv:2003.11563archive 2025-07-28

Harish Tayyar Madabushi, Elena Kochkina, Michael Castelle

The automatic identification of propaganda has gained significance in recent years due to technological and social changes in the way news is generated and consumed. That this task can be addressed effectively using BERT, a powerful new architecture which can be fine-tuned for text classification tasks, is not surprising. However, propaganda detection, like other tasks that deal with news documents and other forms of decontextualized social communication (e.g. sentiment analysis), inherently deals with data whose categories are simultaneously imbalanced and dissimilar. We show that BERT, while capable of handling imbalanced classes with no additional data augmentation, does not generalise well when the training and test data are sufficiently dissimilar (as is often the case with news sources, whose topics evolve over time). We show how to address this problem by providing a statistical measure of similarity between datasets and a method of incorporating cost-weighting into BERT when the training and test sets are dissimilar. We test these methods on the Propaganda Techniques Corpus (PTC) and achieve the second-highest score on sentence-level propaganda classification.

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Classification Data Augmentation General Classification Propaganda detection Sentence Sentence Classification Sentiment Analysis Text Classification text-classification

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Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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