A universal classification model aims to generalize to diverse classification
tasks in both zero and few shot settings. A promising way toward universal
classification is to cast heterogeneous data formats into a dataset-agnostic
"meta-task" (e.g., textual entailment, question answering) then pretrain a
model on the combined meta dataset. The existing work is either pretrained on
specific subsets of classification tasks, or pretrained on both classification
and generation data but the model could not fulfill its potential in
universality and reliability. These also leave a massive amount of annotated
data under-exploited. To fill these gaps, we propose ConEntail, a new framework
for universal zero and few shot classification with supervised contrastive
pretraining. Our unified meta-task for classification is based on nested
entailment. It can be interpreted as "Does sentence a entails [sentence b
entails label c]". This formulation enables us to make better use of 57
annotated classification datasets for supervised contrastive pretraining and
universal evaluation. In this way, ConEntail helps the model (1) absorb
knowledge from different datasets, and (2) gain consistent performance gain
with more pretraining data. In experiments, we compare our model with
discriminative and generative models pretrained on the same dataset. The
results confirm that our framework effectively exploits existing annotated data
and consistently outperforms baselines in both zero (9.4% average improvement)
and few shot settings (3.5% average improvement).

提出了一种基于超级对比学习预处理的新框架 ConEntail，它使用统一的元任务进行分类，该任务基于嵌套蕴含。实验结果表明，该框架可以有效地利用现有的注释数据，在零和少量样本情况下始终优于基线 (分别达到 9.4% 和 3.5% 的平均改进)。