论文标题
反对AI的卡：在填空派对游戏中预测幽默
Cards Against AI: Predicting Humor in a Fill-in-the-blank Party Game
论文作者
论文摘要
幽默是一种天生的社会现象，幽默的话语是由社会和文化上接受的。了解幽默是一个重要的NLP挑战，在人类计算机相互作用中有许多应用。在这项工作中，我们探讨了针对人类的纸牌的幽默感 - 派对游戏，玩家使用可能是令人反感或政治上不正确的卡片填写填写陈述。我们介绍了一个针对人类的300,000张在线纸牌游戏的新颖数据集，包括785k独特的笑话，分析并提供见解。我们训练了机器学习模型，以预测每场比赛的获胜笑话，即使没有任何用户信息，也可以随机地达到两倍的性能（20 \％）。关于判断新颖卡的更艰巨的任务，我们看到模型的概括能力是中等的。有趣的是，我们发现我们的模型主要集中在打孔卡上，而上下文影响很小。分析特征的重要性，我们观察到短暂，粗糙，少年的打孔线往往会获胜。
Humor is an inherently social phenomenon, with humorous utterances shaped by what is socially and culturally accepted. Understanding humor is an important NLP challenge, with many applications to human-computer interactions. In this work we explore humor in the context of Cards Against Humanity -- a party game where players complete fill-in-the-blank statements using cards that can be offensive or politically incorrect. We introduce a novel dataset of 300,000 online games of Cards Against Humanity, including 785K unique jokes, analyze it and provide insights. We trained machine learning models to predict the winning joke per game, achieving performance twice as good (20\%) as random, even without any user information. On the more difficult task of judging novel cards, we see the models' ability to generalize is moderate. Interestingly, we find that our models are primarily focused on punchline card, with the context having little impact. Analyzing feature importance, we observe that short, crude, juvenile punchlines tend to win.
