论文标题
利用少量拍摄文本分类和自然语言推断的披肩问题
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference
论文作者
论文摘要
可以通过提供自然语言的“任务说明”的验证语言模型来以完全无监督的方式解决一些NLP任务（例如Radford等，2019）。尽管这种方法表现不佳，但我们在这项工作中表明，这两个想法可以结合在一起：我们介绍了模式开发培训（PET），这是一种半监督的培训程序，将输入示例重新定义为粘贴风格的示例，以帮助语言模型了解给定的任务。然后，这些短语用于将软标签分配给大量未标记的示例。最后，对由此产生的培训组进行了标准监督培训。对于几种任务和语言，宠物的表现优于监督培训和在低资源环境中的强大半监督方法的幅度很大。
Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with "task descriptions" in natural language (e.g., Radford et al., 2019). While this approach underperforms its supervised counterpart, we show in this work that the two ideas can be combined: We introduce Pattern-Exploiting Training (PET), a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task. These phrases are then used to assign soft labels to a large set of unlabeled examples. Finally, standard supervised training is performed on the resulting training set. For several tasks and languages, PET outperforms supervised training and strong semi-supervised approaches in low-resource settings by a large margin.
