Textual label names (descriptions) are typically semantically rich in many
natural language understanding (NLU) tasks. In this paper, we incorporate the
prompting methodology, which is widely used to enrich model input, into the
label side for the first time. Specifically, we propose a Mask Matching method,
which equips an input with a prompt and its label with another, and then makes
predictions by matching their mask representations. We evaluate our method
extensively on 8 NLU tasks with 14 datasets. The experimental results show that
Mask Matching significantly outperforms its counterparts of fine-tuning and
conventional prompt-tuning, setting up state-of-the-art performances in several
datasets. Mask Matching is particularly good at handling NLU tasks with large
label counts and informative label names. As pioneering efforts that
investigate the label-side prompt, we also discuss open issues for future
study.

我们提出了一种掩码匹配方法，将提示方法首次应用于标签侧，用于处理具有大量标签计数和信息化标签名称的 NLU 任务，并在 8 个 NLU 任务的 14 个数据集上得到了显著的性能提升，成为多个数据集中的最佳方法。