Although humor enriches human lives, some jokes fail to amuse people because of a lack of morality. In this paper, we propose a mechanism capable of selecting humor based on moral criteria. To this end, we first construct a model based on an N-gram corpus and generate joke candidates using various template patterns. We then employ a moral judgement classifier based on a recurrent neural network and utilize the trained model for humor selection. The experimental results obtained from best–worst scaling demonstrate that this scheme is able to generate jokes with moral category labels. We confirmed that jokes about the classifier categorized as Loyalty and Authority, which are regarded as good in our study, are funnier than jokes about Fairness, Purity, Harm, Cheating, and Degradation. Although we did not confirm that there was a difference in the funny level between good and bad moral jokes, the results demonstrate that moral categories of humor can affect the funny level.

尽管幽默丰富了人类的生活，但由于缺乏道德，有些笑话未能使人们开心。在本文中，我们提出了一种能够根据道德标准选择幽默的机制。为此，我们首先基于N- gram语料库构建模型，并使用各种模板模式生成玩笑候选。然后，我们基于递归神经网络采用道德判断分类器，并利用经过训练的模型进行幽默选择。从最差评估得到的实验结果表明，该方案能够产生带有道德类别标签的笑话。我们证实了关于分类作为分类是笑话忠诚和权威，这被认为是好于我们的研究中，比约滑稽的笑话公平，纯度，危害，作弊和退化。虽然我们并没有确认，在之间有趣的水平差好和坏的道德笑话，结果表明，幽默的道德范畴可能会影响搞笑水平。