Much previous work has been done in attempting to identify humor in text. In
this paper we extend that capability by proposing a new task: assessing whether
or not a joke is humorous. We present a novel way of approaching this problem
by building a model that learns to identify humorous jokes based on ratings
gleaned from Reddit pages, consisting of almost 16,000 labeled instances. Using
these ratings to determine the level of humor, we then employ a Transformer
architecture for its advantages in learning from sentence context. We
demonstrate the effectiveness of this approach and show results that are
comparable to human performance. We further demonstrate our model's increased
capabilities on humor identification problems, such as the previously created
datasets for short jokes and puns. These experiments show that this method
outperforms all previous work done on these tasks, with an F-measure of 93.1%
for the Puns dataset and 98.6% on the Short Jokes dataset.

本文提出新的任务：判断一个笑话是否幽默，通过建立基于 Reddit 站点上近 16,000 标记实例的幽默评分模型，使用 Transformer 体系结构实现语句上下文的学习，取得了与人类表现可比较的成果，并在短笑话和双关语数据集上取得了 93.1% 和 98.6% 的 F-measure，这一方法超过了以前用于这些任务的所有先前工作。