论文标题
Ambipun：以模棱两可的背景产生幽默的双关语
AmbiPun: Generating Humorous Puns with Ambiguous Context
论文作者
论文摘要
在本文中，我们提出了一种简单而有效的方法来产生双关语句子，而双关语句子不需要对现有双关语进行任何培训。我们的方法是受幽默理论的启发，即歧义来自上下文而不是双关语本身。鉴于双关语单词的一对定义，我们的模型首先通过反向字典产生相关概念的列表。然后，我们利用一声gpt3生成上下文单词，然后生成联合两个概念上下文单词的双关语。人类评估表明，我们的方法在52 \％的时间内成功产生了PUN，超过了精心制作的基准和最先进的模型。
In this paper, we propose a simple yet effective way to generate pun sentences that does not require any training on existing puns. Our approach is inspired by humor theories that ambiguity comes from the context rather than the pun word itself. Given a pair of definitions of a pun word, our model first produces a list of related concepts through a reverse dictionary. We then utilize one-shot GPT3 to generate context words and then generate puns incorporating context words from both concepts. Human evaluation shows that our method successfully generates pun 52\% of the time, outperforming well-crafted baselines and the state-of-the-art models by a large margin.
