StandUp4AI: Advancements in Automatic Humor Detection
The paper "StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos," presents a novel approach and dataset aimed at improving humor detection in computational models. This work stands out due to its focus on stand-up comedy performances, spanning seven languages: English, French, Spanish, Italian, Portuguese, Hungarian, and Czech. The dataset constitutes over 330 hours of video footage, which represents one of the largest collections available for humor detection tasks.
Dataset and Approach
The authors curated the StandUp4AI dataset by collecting stand-up comedy performances from popular online platforms, focusing on videos with a single comedian and avoiding short-form content like Youtube Shorts. Automatic laughter detection, inspired by existing benchmarks, was employed using state-of-the-art techniques such as the model from Omine et al. (2024). The dataset includes automatically refined audience laughter annotations, complemented by manual verification for model validation.
The research introduces a shift from conventional binary classification to sequence labeling for humor detection. This method labels each word as either leading to laughter or not, thereby capturing the continuous nature of humor and audience reactions peculiar to stand-up comedy.
Methodological Contributions
The paper elaborates on an innovative method for improving the accuracy of laughter detection in video transcripts through the integration of ASR outputs from Whisper and WhisperX systems. Enhanced timestamp verification of laughter events led to improved candidate selection for laughter, which human annotators later evaluated in a subset of videos. The manuscript delineates the methodology for refining laughter detection and establishing a standardized framework for its automated validation using acoustic features.
Additionally, the authors have implemented unimodal sequence labeling approaches using large pre-trained models fine-tuned for this specific laughter prediction task. These models set a baseline for future humor detection research by emphasizing the importance of multilingual training on diverse data.
Results and Implications
The results indicate a substantial advantage in applying a multilingual approach, as opposed to monolingual models, for predicting humor. The enhanced dataset with refined laughter labels improved model predictions, demonstrating the effectiveness of the proposed multimodal techniques. Quantitatively, multilingual models showed a higher F1-score of 42.4 compared to 39.4 achieved by monolingual configurations.
With these findings, the research propels the computational modeling of humor toward a direction that acknowledges linguistic diversity and multimodal contexts. The implications of this work extend beyond improved interactive systems and chatbots, potentially enriching AI's capacity for cultural nuance and enhancing user experience in conversational agents.
Future Directions
Looking ahead, the integration of verbal and non-verbal cues in automatic humor detection is recommended. Further exploration into the cultural variability of humor responses could deepen the insights into humor mechanics across languages and sociocultural contexts. The dataset also invites potential enhancements through multimodal learning, leveraging visible and acoustic features for deeper analysis.
By offering both data and code publicly, the paper encourages continued collaboration and innovation in developing humor-aware artificial intelligence systems. StandUp4AI is poised to be a reference point in humor detection studies, providing groundwork for the exploration of context-aware AI interactions.
