Understanding various humour styles is essential for comprehending the multifaceted nature of humour and its impact on fields such as psychology and artificial intelligence. This understanding has revealed that humour, depending on the style employed, can either have therapeutic or detrimental effects on an individual's health and relationships. Although studies dedicated exclusively to computational-based humour style analysis remain somewhat rare, an expansive body of research thrives within related task, particularly binary humour and sarcasm recognition. In this systematic literature review (SLR), we survey the landscape of computational techniques applied to these related tasks and also uncover their fundamental relevance to humour style analysis. Through this study, we unveil common approaches, illuminate various datasets and evaluation metrics, and effectively navigate the complex terrain of humour research. Our efforts determine potential research gaps and outlined promising directions. Furthermore, the SLR identifies a range of features and computational models that can seamlessly transition from related tasks like binary humour and sarcasm detection to invigorate humour style classification. These features encompass incongruity, sentiment and polarity analysis, ambiguity detection, acoustic nuances, visual cues, contextual insights, and more. The computational models that emerge contain traditional machine learning paradigms, neural network architectures, transformer-based models, and specialised models attuned to the nuances of humour. Finally, the SLR provides access to existing datasets related to humour and sarcasm, facilitating the work of future researchers.
A Survey of Pun Generation: Datasets, Evaluations and Methodologies
School of Computer Science, University of Auckland（奥克兰大学计算机科学学院）；Singapore University of Technology and Design（新加坡科技设计大学）；School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦女王学院电子工程与计算机科学学院）
HCSU: A Dataset and Benchmark for Fine-Grained Historical Calligraphy Style Understanding
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；The Key Laboratory of Embedded System and Service Computing, Ministry of Education（教育部嵌入式系统与服务计算重点实验室）
Words at Play: Benchmarking Audio Pun Understanding in Large Audio-Language Models
School of Computer Science, University of Auckland（奥克兰大学计算机科学学院）；Singapore University of Technology and Design（新加坡科技设计大学）；School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦大学玛丽女王学院电子工程与计算机科学学院）；School of Statistics and Data Science, Nankai University（南开大学统计与数据科学学院）
Korean aegyo speech shows systematic F1 increase to signal childlike qualities
Department of Korean Language and Literature, Duksung Women’s University（韩国语言文学系，杜庆女子大学）；Department of Computational Linguistics, University of Zurich（计算语言学系，苏黎世大学）
PreMind: Multi-Agent Video Understanding for Advanced Indexing of Presentation-style Videos
Bosch Research North America（博世北美研究院）；Bosch Center for Artificial Intelligence (BCAI)（博世人工智能中心）；Department of Computer Science and Engineering, Texas A&M University（德克萨斯农工大学计算机科学与工程学院）
