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Cost Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data

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Table 2: F1 scores on an unseen (not used for train-ing) part of the training set and the development set onBERT using different augmentation techniques.
Table 3: Class-wise precision and recall with and with-out oversampling (OS) achieved on unseen part of thetraining set.
Figure 2: The impact of modifying the minority classweights on the performance on similar (subset of train-ing set) and dissimilar (development) datasets.Themethod of increasing minority class weights is able topush the model towards generalisation while maintain-ing precision.
Table 5: Our results on the FLC task (7th, in bold)alongside those of better performing teams from thecompetition leaderboard.

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