Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieved state-of-the-art performance. However, existing solutions heavily rely on the exploitation of lexical features and their distributional signatures on training data, while neglecting to strengthen the model's ability to adapt to new tasks. In this paper, we propose a novel meta-learning framework integrated with an adversarial domain adaptation network, aiming to improve the adaptive ability of the model and generate high-quality text embedding for new classes. Extensive experiments are conducted on four benchmark datasets and our method demonstrates clear superiority over the state-of-the-art models in all the datasets. In particular, the accuracy of 1-shot and 5-shot classification on the dataset of 20 Newsgroups is boosted from 52.1% to 59.6%, and from 68.3% to 77.8%, respectively.

翻译：元化学习已成为解决微小文本分类和取得最新性能的一种趋势技术,但是,现有解决方案在很大程度上依赖在培训数据中利用词汇特征及其分布特征,而忽视了加强模型适应新任务的能力。在本文件中,我们提议建立一个与对抗性域适应网络相结合的新元学习框架,目的是提高模型的适应能力,为新类别生成高质量的文本。对四个基准数据集进行了广泛的实验,我们的方法在所有数据集中明显优于最先进的模型。特别是,20个新闻组数据集的一发和五发分类的准确性分别从52.1%提高到59.6%和68.3%提高到77.8%。