  Humor understanding is an important and challenging research in natural
language processing. As the popularity of pre-trained language models (PLMs),
some recent work makes preliminary attempts to adopt PLMs for humor recognition
and generation. However, these simple attempts do not substantially answer the
question: {\em whether PLMs are capable of humor understanding?} This paper is
the first work that systematically investigates the humor understanding ability
of PLMs. For this purpose, a comprehensive framework with three evaluation
steps and four evaluation tasks is designed. We also construct a comprehensive
Chinese humor dataset, which can fully meet all the data requirements of the
proposed evaluation framework. Our empirical study on the Chinese humor dataset
yields some valuable observations, which are of great guiding value for future
optimization of PLMs in humor understanding and generation.
