JTMA: Joint Multimodal Feature Fusion and Temporal Multi-head Attention for Humor Detection-Reference-Cited by-同舟云学术

JTMA: Joint Multimodal Feature Fusion and Temporal Multi-head Attention for Humor Detection

Published:2023-10-29 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 4th on Multimodal Sentiment Analysis Challenge and Workshop: Mimicked Emotions, Humour and Personalisation
language:
Short-container-title:

Author:

Li Qi¹^ORCID,Xu Yangyang²^ORCID,Zhao Zhuoer¹^ORCID,Tang Shulei¹^ORCID,Zhang Feixiang¹^ORCID,Wang Ruotong¹^ORCID,Sun Xiao³^ORCID,Wang Meng⁴^ORCID

Affiliation:

1. Anhui University, Hefei, China

2. University of Science and Technology of China, Hefei, China

3. Hefei University of Technology, Hefei Comprehensive National Science Center, & ZhongJuYuan Intelligent Technology Co., Ltd, Hefei, China

4. Hefei University of Technology & Hefei Comprehensive National Science Center, Hefei, China

Funder

National Key R&D Programme of China

Major Project of Anhui Province

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3606039.3613112

Reference28 articles.

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2. Alexei Baevski , Yuhao Zhou , Abdelrahman Mohamed , and Michael Auli . 2020. wav2vec 2.0: A framework for self-supervised learning of speech representations. Advances in neural information processing systems , Vol. 33 ( 2020 ), 12449--12460. Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli. 2020. wav2vec 2.0: A framework for self-supervised learning of speech representations. Advances in neural information processing systems , Vol. 33 (2020), 12449--12460.

3. Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis

4. Transformer Encoder With Multi-Modal Multi-Head Attention for Continuous Affect Recognition

5. Multimodal Multi-task Learning for Dimensional and Continuous Emotion Recognition

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