Commonality and Individuality! Integrating Humor Commonality with Speaker Individuality for Humor Recognition
Haohao Zhu, Junyu Lu, Zeyuan Zeng, Zewen Bai, Xiaokun Zhang, Liang Yang, Hongfei Lin
TL;DR
The paper tackles humor recognition by jointly modeling multifaceted humor commonality and speaker individuality. It introduces CIHR, an architecture combining Humor Commonality Analysis via LLM prompts, static/dynamic speaker profiling, and Static/Dynamic Fusion to fuse these signals for improved humor detection. Empirical results on HumorWB show CIHR outperforms a wide range of baselines, with ablations confirming the importance of each component and their interactions. The work highlights the practical value of integrating general humor signals with speaker-specific traits, while acknowledging dataset biases and the need for broader evaluation with more diverse data and models. Overall, CIHR advances humor recognition toward more nuanced, personalized understanding of humor in user-generated text.
Abstract
Humor recognition aims to identify whether a specific speaker's text is humorous. Current methods for humor recognition mainly suffer from two limitations: (1) they solely focus on one aspect of humor commonalities, ignoring the multifaceted nature of humor; and (2) they typically overlook the critical role of speaker individuality, which is essential for a comprehensive understanding of humor expressions. To bridge these gaps, we introduce the Commonality and Individuality Incorporated Network for Humor Recognition (CIHR), a novel model designed to enhance humor recognition by integrating multifaceted humor commonalities with the distinctive individuality of speakers. The CIHR features a Humor Commonality Analysis module that explores various perspectives of multifaceted humor commonality within user texts, and a Speaker Individuality Extraction module that captures both static and dynamic aspects of a speaker's profile to accurately model their distinctive individuality. Additionally, Static and Dynamic Fusion modules are introduced to effectively incorporate the humor commonality with speaker's individuality in the humor recognition process. Extensive experiments demonstrate the effectiveness of CIHR, underscoring the importance of concurrently addressing both multifaceted humor commonality and distinctive speaker individuality in humor recognition.