Recent advances in large pretrained language models have increased attention to zero-shot text classification. In particular, models finetuned on natural language inference datasets have been widely adopted as zero-shot classifiers due to their promising results and off-the-shelf availability. However, the fact that such models are unfamiliar with the target task can lead to instability and performance issues. We propose a plug-and-play method to bridge this gap using a simple self-training approach, requiring only the class names along with an unlabeled dataset, and without the need for domain expertise or trial and error. We show that fine-tuning the zero-shot classifier on its most confident predictions leads to significant performance gains across a wide range of text classification tasks, presumably since self-training adapts the zero-shot model to the task at hand.
翻译:大型预先培训的语文模型最近的进展使人们更加注意零发文本分类,特别是,对自然语言推断数据集进行微调的模型因其有希望的结果和现成的可得性而被广泛采用为零发分类器。然而,由于这些模型不熟悉目标任务,可能会导致不稳定和性能问题。我们建议一种插座和游戏方法,使用简单的自我培训方法缩小这一差距,只要求类名和未贴标签的数据集,而不需要域域专门知识或试验和错误。我们表明,根据最有信心的预测对零发分类器进行微调,可导致在广泛的文本分类任务中取得显著的业绩收益,大概因为自修使零发模型适应手头的任务。