Multimodal Humor Understanding: Curating a Dataset of Funny YouTube Videos and Improving Language Models' Ability to Explain Video Humor

Core Concepts

Curating a dataset of 10,136 user-generated funny short-form videos from YouTube, annotated with timestamps and explanations of humorous moments, and developing a zero-shot video-to-text prompting approach to improve language models' ability to explain video humor.

Abstract

The authors curate the ExFunTube dataset, which consists of 10,136 user-generated funny short-form videos from YouTube. Each video is annotated with timestamps and text explanations of the funny moments. The dataset aims to assess how well AI models can understand and explain video humor.
The authors develop a four-step video filtering pipeline to ensure that the collected videos exhibit multimodal humor, with both verbal and visual elements contributing to the humor. The pipeline uses GPT-3.5 to verify the presence of humor in the videos.
The authors then explore an approach to explain video humor by converting the video content into fine-grained text and leveraging powerful language models (LLMs) in a zero-shot manner. They design a zero-shot video-to-text prompting that extracts information from the visual, speech, and sound modalities of the videos and arranges them chronologically as a text prompt for the LLMs.
The authors evaluate the performance of their approach using three different methods: model-based automatic scores, rationale quality experiments, and human evaluations. The results show that their prompting approach significantly improves the humor explanation performance of three LLMs, including one zero-shot GPT-3.5 and two finetuned T5 and BART models, compared to text-only baselines and other multimodal approaches.
The authors also analyze the LLMs' performance across different humor categories and find that their prompting approach is particularly effective in explaining humor that heavily relies on visual elements, such as clownish humor, visual gags, and slapstick.

Stats

"A white dog with blue eyes being fed some kind of flower."
"A person feeds a flower to a white husky."
"The husky dog eats a flower with his paw."
"A person holding out his thumb to a husky."

Quotes

None

Key Insights Distilled From

Can Language Models Laugh at YouTube Short-form Videos?

by Dayoon Ko,Sa... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2310.14159.pdf

Deeper Inquiries

How can the dataset be further expanded to cover a wider range of humor types and cultural contexts?

To expand the dataset to cover a wider range of humor types and cultural contexts, several strategies can be implemented:

Diversifying Humor Types: Include a broader spectrum of humor types such as satire, irony, parody, wordplay, and dark humor. This can be achieved by sourcing videos from a variety of sources and genres that cater to different forms of humor.
Cultural Representation: Incorporate videos from diverse cultural backgrounds to ensure a more inclusive dataset. This can involve sourcing content from different regions, languages, and cultural contexts to capture a global perspective on humor.
Collaboration with Content Creators: Partnering with content creators from various cultural backgrounds can help in curating videos that reflect specific cultural nuances and humor styles.
User Contributions: Encourage user submissions to the dataset, allowing individuals to share videos that they find humorous, thereby incorporating a wide range of humor types and cultural references.

How can the multimodal understanding of humor be improved by incorporating additional modalities, such as facial expressions or body language?

Incorporating additional modalities like facial expressions and body language can enhance the multimodal understanding of humor in the following ways:

Facial Expressions: Analyzing facial expressions can provide valuable cues about the emotional context of humor. Emotion recognition technology can be used to detect smiles, laughter, or other facial expressions associated with humor.
Body Language: Body language plays a significant role in conveying humor, such as gestures, posture, and movements. Utilizing pose estimation and gesture recognition algorithms can help in understanding the physical aspects of humor.
Multimodal Fusion: By integrating data from multiple modalities like audio, visual, facial expressions, and body language, a more comprehensive understanding of humor can be achieved. Fusion techniques such as late fusion or early fusion can be employed to combine information from different modalities.
Machine Learning Models: Training machine learning models to recognize patterns in facial expressions and body language associated with humor can improve the overall multimodal understanding. Deep learning models like CNNs and RNNs can be utilized for this purpose.

What are the potential applications of improved humor understanding in AI systems, beyond video recommendation and generation?

Improved humor understanding in AI systems can have diverse applications beyond video recommendation and generation:

Chatbots and Virtual Assistants: AI-powered chatbots and virtual assistants can incorporate humor to engage users and provide more personalized interactions. Understanding humor can help in creating more natural and engaging conversations.
Content Creation: AI systems with humor understanding capabilities can assist in generating creative and humorous content for marketing, advertising, and entertainment industries.
Emotion Recognition: Humor understanding can contribute to better emotion recognition in AI systems, leading to more empathetic and context-aware responses.
Social Skills Training: AI systems can be used for social skills training by providing feedback on humor comprehension and expression, aiding individuals in improving their communication skills.
Healthcare and Therapy: Incorporating humor in AI systems used in healthcare and therapy settings can help in promoting emotional well-being and stress relief among users. It can also be beneficial in therapeutic interventions for mental health conditions.