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.

对于预训练语言模型（PLMs）在幽默理解上的能力进行了首次系统调查，通过构建一个综合的评估框架、三个评估步骤和四个评估任务，并构建了一个全面的中文幽默数据集，为未来优化 PLMs 在幽默理解和生成中提供了有价值的观察结果。

预训练语言模型能理解中文幽默吗？

Can Pre-trained Language Models Understand Chinese Humor?

The tasks of humor understanding and generation are challenging and
subjective even for humans, requiring commonsense and real-world knowledge to
master. Puns, in particular, add the challenge of fusing that knowledge with
the ability to interpret lexical-semantic ambiguity. In this paper, we present
the ExPUNations (ExPUN) dataset, in which we augment an existing dataset of
puns with detailed crowdsourced annotations of keywords denoting the most
distinctive words that make the text funny, pun explanations describing why the
text is funny, and fine-grained funniness ratings. This is the first humor
dataset with such extensive and fine-grained annotations specifically for puns.
Based on these annotations, we propose two tasks: explanation generation to aid
with pun classification and keyword-conditioned pun generation, to challenge
the current state-of-the-art natural language understanding and generation
models' ability to understand and generate humor. We showcase that the
annotated keywords we collect are helpful for generating better novel humorous
texts in human evaluation, and that our natural language explanations can be
leveraged to improve both the accuracy and robustness of humor classifiers.

本文提出了一个用于理解和生成语言幽默的数据集 ExPUN，其中包含有关标记单词的详细注释，用于描述开玩笑的原因的双关语解释以及细粒度的幽默等级评分。同时，本文提出了解释生成和关键字条件的双关语生成两个任务，以挑战目前自然语言理解和生成模型的理解和生成幽默的能力，并展示了我们收集的注释关键字有助于生成更好的新颖幽默文本。