Well-defined jokes can be divided neatly into a setup and a punchline. While most works on humor today talk about a joke as a whole, the idea of generating punchlines to a setup has applications in conversational humor, where funny remarks usually occur with a non-funny context. Thus, this paper is based around two core concepts: Classification and the Generation of a punchline from a particular setup based on the Incongruity Theory. We first implement a feature-based machine learning model to classify humor. For humor generation, we use a neural model, and then merge the classical rule-based approaches with the neural approach to create a hybrid model. The idea behind being: combining insights gained from other tasks with the setup-punchline model and thus applying it to existing text generation approaches. We then use and compare our model with human written jokes with the help of human evaluators in a double-blind study.

中文翻译：

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走向对话式幽默分析与设计

定义明确的笑话可以整齐地分为设置和重点。尽管当今大多数关于幽默的著作都谈论一个笑话，但为设置生成打孔线的想法在会话幽默中也有应用，在这种情况下，有趣的言论通常是在非有趣的上下文中发生的。因此，本文基于两个核心概念：分类和基于不相容性理论的特定设置生成的打孔线。我们首先实现基于特征的机器学习模型来对幽默进行分类。对于幽默生成，我们使用神经模型，然后将基于规则的经典方法与神经方法合并以创建混合模型。其背后的想法是：将从其他任务中获得的见解与设置冲压线模型相结合，然后将其应用于现有的文本生成方法。