Pith. sign in

REVIEW 3 cited by

UR-FUNNY: A Multimodal Language Dataset for Understanding Humor

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1904.06618 v1 pith:PUVVH3K4 submitted 2019-04-14 cs.LG cs.CLstat.ML

Md Kamrul Hasan , Wasifur Rahman , Amir Zadeh , Jianyuan Zhong , Md Iftekhar Tanveer , Louis-Philippe Morency , Mohammed (Ehsan) Hoque This is my paper
classification cs.LGcs.CLstat.ML
keywords multimodalhumorlanguagedatasetnaturalresearchunderstandingur-funny
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it is an understudied area. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research.

Discussion (0). Sign in to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting

    cs.AI 2025-08 conditional novelty 6.0 of 10

    Benchmarks seven open-source audio-video-text MLLMs on six affective datasets and shows a generative-knowledge prompting step improves fine-tuned emotion recognition.

  2. StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A new 334-hour, 7-language stand-up comedy dataset with word-level laughter labels and baseline humor detection models.

  3. Efficient Quantification of Multimodal Interaction at Sample Level

    cs.LG 2025-06 conditional novelty 5.0 of 10

    A lightweight estimator quantifies sample-level multimodal interactions (redundancy, uniqueness, synergy) in continuous distributions and uses them for data partitioning, distillation, and ensembling.

Pith tools