Commonality and Individuality! Integrating Humor Commonality with Speaker Individuality for Humor Recognition
- URL: http://arxiv.org/abs/2502.04960v1
- Date: Fri, 07 Feb 2025 14:23:49 GMT
- Title: Commonality and Individuality! Integrating Humor Commonality with Speaker Individuality for Humor Recognition
- Authors: Haohao Zhu, Junyu Lu, Zeyuan Zeng, Zewen Bai, Xiaokun Zhang, Liang Yang, Hongfei Lin,
- Abstract summary: The Commonality and Individuality Incorporated Network for Humor Recognition (CIHR) is a novel model designed to enhance humor recognition.
The CIHR features a Humor Commonality Analysis module that explores various perspectives of multifaceted humor commonality within user texts.
A Speaker Individuality Extraction module captures both static and dynamic aspects of a speaker's profile to accurately model their distinctive individuality.
- Score: 16.17139518077975
- License:
- Abstract: Humor recognition aims to identify whether a specific speaker's text is humorous. Current methods for humor recognition mainly suffer from two limitations: (1) they solely focus on one aspect of humor commonalities, ignoring the multifaceted nature of humor; and (2) they typically overlook the critical role of speaker individuality, which is essential for a comprehensive understanding of humor expressions. To bridge these gaps, we introduce the Commonality and Individuality Incorporated Network for Humor Recognition (CIHR), a novel model designed to enhance humor recognition by integrating multifaceted humor commonalities with the distinctive individuality of speakers. The CIHR features a Humor Commonality Analysis module that explores various perspectives of multifaceted humor commonality within user texts, and a Speaker Individuality Extraction module that captures both static and dynamic aspects of a speaker's profile to accurately model their distinctive individuality. Additionally, Static and Dynamic Fusion modules are introduced to effectively incorporate the humor commonality with speaker's individuality in the humor recognition process. Extensive experiments demonstrate the effectiveness of CIHR, underscoring the importance of concurrently addressing both multifaceted humor commonality and distinctive speaker individuality in humor recognition.
Related papers
- Nonverbal Interaction Detection [83.40522919429337]
This work addresses a new challenge of understanding human nonverbal interaction in social contexts.
We contribute a novel large-scale dataset, called NVI, which is meticulously annotated to include bounding boxes for humans and corresponding social groups.
Second, we establish a new task NVI-DET for nonverbal interaction detection, which is formalized as identifying triplets in the form individual, group, interaction> from images.
Third, we propose a nonverbal interaction detection hypergraph (NVI-DEHR), a new approach that explicitly models high-order nonverbal interactions using hypergraphs.
arXiv Detail & Related papers (2024-07-11T02:14:06Z) - Humor Mechanics: Advancing Humor Generation with Multistep Reasoning [11.525355831490828]
We develop a working prototype for humor generation using multi-step reasoning.
We compare our approach with human-created jokes, zero-shot GPT-4 generated humor, and other baselines.
Our findings demonstrate that the multi-step reasoning approach consistently improves the quality of generated humor.
arXiv Detail & Related papers (2024-05-12T13:00:14Z) - Emotional Listener Portrait: Realistic Listener Motion Simulation in
Conversation [50.35367785674921]
Listener head generation centers on generating non-verbal behaviors of a listener in reference to the information delivered by a speaker.
A significant challenge when generating such responses is the non-deterministic nature of fine-grained facial expressions during a conversation.
We propose the Emotional Listener Portrait (ELP), which treats each fine-grained facial motion as a composition of several discrete motion-codewords.
Our ELP model can not only automatically generate natural and diverse responses toward a given speaker via sampling from the learned distribution but also generate controllable responses with a predetermined attitude.
arXiv Detail & Related papers (2023-09-29T18:18:32Z) - Mining Effective Features Using Quantum Entropy for Humor Recognition [19.20228079459944]
Humor recognition has been extensively studied with different methods in the past years.
In this paper, inspired by the incongruity theory, any joke can be divided into two components (the setup and the punchline)
We use density matrices to represent the semantic uncertainty of the setup and the punchline, respectively, and design QE-Uncertainty and QE-Incongruity with the help of quantum entropy as features for humor recognition.
arXiv Detail & Related papers (2023-02-07T19:09:09Z) - This joke is [MASK]: Recognizing Humor and Offense with Prompting [9.745213455946324]
Humor is a magnetic component in everyday human interactions and communications.
We investigate the effectiveness of prompting, a new transfer learning paradigm for NLP, for humor recognition.
arXiv Detail & Related papers (2022-10-25T13:02:45Z) - Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results [84.37263300062597]
Humor is a substantial element of human social behavior, affect, and cognition.
Current methods of humor detection have been exclusively based on staged data, making them inadequate for "real-world" applications.
We contribute to addressing this deficiency by introducing the novel Passau-Spontaneous Football Coach Humor dataset, comprising about 11 hours of recordings.
arXiv Detail & Related papers (2022-09-28T17:36:47Z) - M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in
Conversations [72.81164101048181]
We propose a dataset for Multimodal Multiparty Hindi Humor (M2H2) recognition in conversations containing 6,191 utterances from 13 episodes of a very popular TV series "Shrimaan Shrimati Phir Se"
Each utterance is annotated with humor/non-humor labels and encompasses acoustic, visual, and textual modalities.
The empirical results on M2H2 dataset demonstrate that multimodal information complements unimodal information for humor recognition.
arXiv Detail & Related papers (2021-08-03T02:54:09Z) - DeHumor: Visual Analytics for Decomposing Humor [36.300283476950796]
We develop DeHumor, a visual system for analyzing humorous behaviors in public speaking.
To intuitively reveal the building blocks of each concrete example, DeHumor decomposes each humorous video into multimodal features.
We show that DeHumor is able to highlight various building blocks of humor examples.
arXiv Detail & Related papers (2021-07-18T04:01:07Z) - Federated Learning with Diversified Preference for Humor Recognition [40.89453484353102]
We propose the FedHumor approach to recognize humorous text contents in a personalized manner through federated learning (FL)
Experiments demonstrate significant advantages of FedHumor in recognizing humor contents accurately for people with diverse humor preferences compared to 9 state-of-the-art humor recognition approaches.
arXiv Detail & Related papers (2020-12-03T03:24:24Z) - "The Boating Store Had Its Best Sail Ever": Pronunciation-attentive
Contextualized Pun Recognition [80.59427655743092]
We propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor.
PCPR derives contextualized representation for each word in a sentence by capturing the association between the surrounding context and its corresponding phonetic symbols.
Results demonstrate that the proposed approach significantly outperforms the state-of-the-art methods in pun detection and location tasks.
arXiv Detail & Related papers (2020-04-29T20:12:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.