We introduce, release, and analyze a new dataset, called Humicroedit, for
research in computational humor. Our publicly available data consists of
regular English news headlines paired with versions of the same headlines that
contain simple replacement edits designed to make them funny. We carefully
curated crowdsourced editors to create funny headlines and judges to score a to
a total of 15,095 edited headlines, with five judges per headline. The simple
edits, usually just a single word replacement, mean we can apply
straightforward analysis techniques to determine what makes our edited
headlines humorous. We show how the data support classic theories of humor,
such as incongruity, superiority, and setup/punchline. Finally, we develop
baseline classifiers that can predict whether or not an edited headline is
funny, which is a first step toward automatically generating humorous headlines
as an approach to creating topical humor.

介绍了一份名为 Humicroedit 的新数据集，用于计算幽默研究。该数据集包含英文新闻标题及其被设计用来制造幽默的简单编辑版本。通过对数据集进行分析，基于传统幽默理论，如不协调、优越感和设置 / 串行，我们开发了基准分类器，可以预测编辑后的标题是否有趣，这是自动生成有趣新闻标题的第一步。