预训练语言模型能理解中文幽默吗？
Can Pre-trained Language Models Understand Chinese Humor?
AI总结：
本文首次系统评估预训练语言模型的中文幽默理解能力，提出三步骤四任务的评估框架并构建中文幽默数据集，实验观察为后续幽默理解与生成优化提供指导。
摘要：
幽默理解是自然语言处理中一项重要且具有挑战性的研究。随着预训练语言模型（PLMs）的流行，近期一些工作初步尝试将PLMs用于幽默识别与生成。然而，这些简单的尝试并未实质性地回答这一问题：PLMs是否具备幽默理解能力？本文是首项系统研究PLMs幽默理解能力的工作。为此，我们设计了一个包含三个评估步骤和四项评估任务的综合框架。我们还构建了一个全面的中文幽默数据集，能够充分满足所提评估框架的全部数据需求。我们在该中文幽默数据集上的实证研究得出了一些有价值的观察结果，这些结果对未来优化PLMs在幽默理解与生成方面的能力具有重要的指导价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
