We propose a unified framework to generate both homophonic and homographic puns to resolve the split-up in existing works. Specifically, we incorporate three linguistic attributes of puns to the language models: ambiguity, distinctiveness, and surprise. Our framework consists of three parts: 1) a context words/phrases selector to promote the aforementioned attributes, 2) a generation model trained on non-pun sentences to incorporate the context words/phrases into the generation output, and 3) a label predictor that learns the structure of puns which is used to steer the generation model at inference time. Evaluation results on both pun types demonstrate the efficacy of our model over strong baselines.

翻译：我们提出一个统一的框架来生成同音和全息图标本,以解决现有作品的分裂问题。具体地说,我们将三个语种特征的标语特性纳入语言模式:模糊、独特和出乎意料。我们的框架由三部分组成:(1) 用于促进上述属性的上下文单词/词句选择器;(2) 受过非普语句培训的一代模式,以将上下文单词/词句纳入生成输出;(3) 标签预测器,以学习用来在推论时间指导生成模型的标语结构。两种语种的评价结果都显示了我们模型在强大基线之上的功效。