THInC：一个理论驱动的计算幽默检测框架
THInC: A Theory-Driven Framework for Computational Humor Detection
AI总结：
本文提出理论驱动的可解释幽默检测框架THInC，通过集成代表不同幽默理论的GA2M分类器并构建代理特征，实现F1值0.85的幽默分类，并支持对理论有效性的定量分析。
摘要：
幽默是人类交流与认知的一个基本方面，因为它在社会互动中发挥着关键作用。尽管关于幽默的理论已经发展了数个世纪，但至今仍未就某一单一、全面的幽默理论达成一致。同样地，尽管大语言模型近期取得了进展，以计算方式识别幽默仍然是一项重大挑战。此外，大多数检测幽默的计算方法并非基于现有的幽默理论。本文通过构建一个可解释的幽默分类框架，为弥合幽默理论研究与计算幽默检测之间长期存在的鸿沟做出了贡献，该框架以多种幽默理论为基础，称为THInC（Theory-driven Humor Interpretation and Classification，理论驱动的幽默解释与分类）。THInC集成了可解释的GA2M分类器，每个分类器代表一种不同的幽默理论。我们设计了一个透明的流程，以主动创建能够定量反映理论不同方面的代理特征。该框架的一个实现取得了0.85的F1分数。该框架的关联可解释性使得能够分析代理特征的有效性、笑话特征与理论之间的一致性，以及识别具有全局贡献的特征。本文标志着在创建一种由多种幽默理论指导的幽默检测框架方面的开创性努力，并为未来理论驱动的幽默分类进展提供了基础。它也是在定量方式下自动比较幽默理论的第一步。
英文摘要：
Humor is a fundamental aspect of human communication and cognition, as it plays a crucial role in social engagement. Although theories about humor have evolved over centuries, there is still no agreement on a single, comprehensive humor theory. Likewise, computationally recognizing humor remains a significant challenge despite recent advances in large language models. Moreover, most computational approaches to detecting humor are not based on existing humor theories. This paper contributes to bridging this long-standing gap between humor theory research and computational humor detection by creating an interpretable framework for humor classification, grounded in multiple humor theories, called THInC (Theory-driven Humor Interpretation and Classification). THInC ensembles interpretable GA2M classifiers, each representing a different humor theory. We engineered a transparent flow to actively create proxy features that quantitatively reflect different aspects of theories. An implementation of this framework achieves an F1 score of 0.85. The associative interpretability of the framework enables analysis of proxy efficacy, alignment of joke features with theories, and identification of globally contributing features. This paper marks a pioneering effort in creating a humor detection framework that is informed by diverse humor theories and offers a foundation for future advancements in theory-driven humor classification. It also serves as a first step in automatically comparing humor theories in a quantitative manner.
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。
