  Pre-trained language models (PLMs) have seen tremendous success in text
classification (TC) problems in the context of Natural Language Processing
(NLP). In many real-world text classification tasks, the class definitions
being learned do not remain constant but rather change with time - this is
known as Concept Shift. Most techniques for handling concept shift rely on
retraining the old classifiers with the newly labelled data. However, given the
amount of training data required to fine-tune large DL models for the new
concepts, the associated labelling costs can be prohibitively expensive and
time consuming. In this work, we propose a reformulation, converting vanilla
classification into an entailment-style problem that requires significantly
less data to re-train the text classifier to adapt to new concepts. We
demonstrate the effectiveness of our proposed method on both real world &
synthetic datasets achieving absolute F1 gains upto 7% and 40% respectively in
few-shot settings. Further, upon deployment, our solution also helped save 75%
of labeling costs overall.
