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。