Abstract
The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, human-centeredness, and lexical centrality most strongly match the funniest captions, followed by positive sentiment. These results are useful for understanding humor and also in the design of more engaging conversational agents in text and multimodal (vision+text) systems. As part of this work, a large set of cartoons and captions is being made available to the community.
Abstract (translated by Google)
纽约客每周发行一张无字幕的漫画。超过5000名读者为其提供字幕。编辑选择其中三个，并要求读者选择最有趣的一个。我们描述了一个比较十几个自动方法来选择最有趣的标题的实验。我们显示消极的情绪，以人为本，和词汇中心性最有力匹配最有趣的字幕，其次是积极的情绪。这些结果对于理解幽默以及在文本和多模式（视觉+文本）系统中更有吸引力的会话代理的设计是有用的。作为这项工作的一部分，一大批漫画和标题正在提供给社区。
URL
https://arxiv.org/abs/1506.08126