论文标题
不确定性和令人惊讶的共同发出重点：利用基于不一致性的功能以识别幽默
Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition
论文作者
论文摘要
使用数据驱动的方法将幽默识别作为文本分类问题进行了广泛研究。但是，大多数现有的工作都没有检查了解幽默的实际笑话机制。我们将任何笑话分解为两个不同的组成部分：设置和重点，并进一步探索它们之间的特殊关系。受幽默不一致的理论的启发，我们将设置建模为发展语义不确定性的一部分，并且爆炸线破坏了受众的期望。借助越来越强大的语言模型，我们能够将设置与重点一起馈入GPT-2语言模型，并计算笑话的不确定性和惊人价值。通过在Semeval 2021任务7数据集上进行实验，我们发现这两个功能具有更好的功能，可以与现有基线相比，从非笑话讲笑话。
Humor recognition has been widely studied as a text classification problem using data-driven approaches. However, most existing work does not examine the actual joke mechanism to understand humor. We break down any joke into two distinct components: the set-up and the punchline, and further explore the special relationship between them. Inspired by the incongruity theory of humor, we model the set-up as the part developing semantic uncertainty, and the punchline disrupting audience expectations. With increasingly powerful language models, we were able to feed the set-up along with the punchline into the GPT-2 language model, and calculate the uncertainty and surprisal values of the jokes. By conducting experiments on the SemEval 2021 Task 7 dataset, we found that these two features have better capabilities of telling jokes from non-jokes, compared with existing baselines.
