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Text classification is usually studied by labeling natural language textswith relevant categories from a predefined set. In the real world, new classesmight keep challenging the existing system with limited labeled data. Thesystem should be intelligent enough to recognize upcoming new classes with afew examples. In this work, we define a new task in the NLP domain, incrementalfew-shot text classification, where the system incrementally handles multiplerounds of new classes. For each round, there is a batch of new classes with afew labeled examples per class. Two major challenges exist in this new task:(i) For the learning process, the system should incrementally learn new classesround by round without re-training on the examples of preceding classes; (ii)For the performance, the system should perform well on new classes without muchloss on preceding classes. In addition to formulating the new task, we alsorelease two benchmark datasets in the incremental few-shot setting: intentclassification and relation classification. Moreover, we propose two entailmentapproaches, ENTAILMENT and HYBRID, which show promise for solving this novelproblem.

Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System