Text classification is usually studied by labeling natural language texts
with relevant categories from a predefined set. In the real world, new classes
might keep challenging the existing system with limited labeled data. The
system should be intelligent enough to recognize upcoming new classes with a
few examples. In this work, we define a new task in the NLP domain, incremental
few-shot text classification, where the system incrementally handles multiple
rounds of new classes. For each round, there is a batch of new classes with a
few labeled examples per class. Two major challenges exist in this new task:
(i) For the learning process, the system should incrementally learn new classes
round by round without re-training on the examples of preceding classes; (ii)
For the performance, the system should perform well on new classes without much
loss on preceding classes. In addition to formulating the new task, we also
release two benchmark datasets in the incremental few-shot setting: intent
classification and relation classification. Moreover, we propose two entailment
approaches, ENTAILMENT and HYBRID, which show promise for solving this novel
problem.

研究了一种新的 NLP 任务：增量少样本文本分类，挑战是系统需要在不重新训练之前的类别示例的情况下轮换增量学习新类别，并在新类别下表现良好，同时不影响之前类别的分类，并在此基础上，提出了两种蕴含方法来解决这个问题，并公开了两个基准数据集。