Go to ACL ARR 2025 February homepageBottleHumor: Self-Informed Humor Explanation using the Information Bottleneck Principle
Abstract: Humor is prevalent in online communications and it often relies on more than one modality (e.g., cartoons and memes).
Interpreting humor in multimodal settings requires drawing on diverse types of knowledge, including metaphorical, sociocultural, and commonsense knowledge. However, identifying the most useful knowledge remains an open question.
We introduce BottleHumor, a method inspired by the information bottleneck principle that elicits relevant world knowledge from vision and language models which is iteratively refined for generating an explanation of the humor in an unsupervised manner. Our experiments on three datasets confirm the advantage of our method over a range of baselines.
Our method can further be adapted in the future for additional tasks that can benefit from eliciting and conditioning on relevant world knowledge and open new research avenues in this direction.
Paper Type: Long
Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond
Research Area Keywords: vision language navigation, multimodality, free-text/natural language explanations
Contribution Types: NLP engineering experiment
Languages Studied: English
Submission Number: 4899
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