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MuVaC: AVariational Causal Framework for Multimodal Sarcasm Understanding in Dialogues

Diandian Guo, Fangfang Yuan, Cong Cao, Xixun Lin, Chuan Zhou, Hao Peng, Yanan Cao, Yanbing Liu

TL;DR

MuVaC reframes multimodal sarcasm understanding as a variational causal inference problem that links MuSE explanations to MSD detection via a latent latent factor and a front-door causal path. It introduces an alignment-then-fusion multimodal fusion module and an explanation-aware training regime with ELBO-based objectives, enforcing consistency between generated explanations and detection outcomes. The approach achieves state-of-the-art results on MSD and MuSE across MUStARD, MUStARD++, and WITS, with notable F1 gains on MUStARD++ and strong semantic-quality metrics for explanations. This work provides a causally grounded, interpretable framework for robust sarcasm understanding with potential for broader multimodal reasoning tasks.

Abstract

The prevalence of sarcasm in multimodal dialogues on the social platforms presents a crucial yet challenging task for understanding the true intent behind online content. Comprehensive sarcasm analysis requires two key aspects: Multimodal Sarcasm Detection (MSD) and Multimodal Sarcasm Explanation (MuSE). Intuitively, the act of detection is the result of the reasoning process that explains the sarcasm. Current research predominantly focuses on addressing either MSD or MuSE as a single task. Even though some recent work has attempted to integrate these tasks, their inherent causal dependency is often overlooked. To bridge this gap, we propose MuVaC, a variational causal inference framework that mimics human cognitive mechanisms for understanding sarcasm, enabling robust multimodal feature learning to jointly optimize MSD and MuSE. Specifically, we first model MSD and MuSE from the perspective of structural causal models, establishing variational causal pathways to define the objectives for joint optimization. Next, we design an alignment-then-fusion approach to integrate multimodal features, providing robust fusion representations for sarcasm detection and explanation generation. Finally, we enhance the reasoning trustworthiness by ensuring consistency between detection results and explanations. Experimental results demonstrate the superiority of MuVaC in public datasets, offering a new perspective for understanding multimodal sarcasm.

MuVaC: AVariational Causal Framework for Multimodal Sarcasm Understanding in Dialogues

TL;DR

MuVaC reframes multimodal sarcasm understanding as a variational causal inference problem that links MuSE explanations to MSD detection via a latent latent factor and a front-door causal path. It introduces an alignment-then-fusion multimodal fusion module and an explanation-aware training regime with ELBO-based objectives, enforcing consistency between generated explanations and detection outcomes. The approach achieves state-of-the-art results on MSD and MuSE across MUStARD, MUStARD++, and WITS, with notable F1 gains on MUStARD++ and strong semantic-quality metrics for explanations. This work provides a causally grounded, interpretable framework for robust sarcasm understanding with potential for broader multimodal reasoning tasks.

Abstract

The prevalence of sarcasm in multimodal dialogues on the social platforms presents a crucial yet challenging task for understanding the true intent behind online content. Comprehensive sarcasm analysis requires two key aspects: Multimodal Sarcasm Detection (MSD) and Multimodal Sarcasm Explanation (MuSE). Intuitively, the act of detection is the result of the reasoning process that explains the sarcasm. Current research predominantly focuses on addressing either MSD or MuSE as a single task. Even though some recent work has attempted to integrate these tasks, their inherent causal dependency is often overlooked. To bridge this gap, we propose MuVaC, a variational causal inference framework that mimics human cognitive mechanisms for understanding sarcasm, enabling robust multimodal feature learning to jointly optimize MSD and MuSE. Specifically, we first model MSD and MuSE from the perspective of structural causal models, establishing variational causal pathways to define the objectives for joint optimization. Next, we design an alignment-then-fusion approach to integrate multimodal features, providing robust fusion representations for sarcasm detection and explanation generation. Finally, we enhance the reasoning trustworthiness by ensuring consistency between detection results and explanations. Experimental results demonstrate the superiority of MuVaC in public datasets, offering a new perspective for understanding multimodal sarcasm.
Paper Structure (32 sections, 1 theorem, 18 equations, 7 figures, 12 tables, 1 algorithm)

This paper contains 32 sections, 1 theorem, 18 equations, 7 figures, 12 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Task Definition
  5. Variational Causal View for MuSE and MSD
  6. Multimodal Feature Extraction
  7. Multimodal Feature Fusion
  8. Causal Explanation Generation
  9. Causal Feature Extraction
  10. Causal-Informed Sarcasm Detection
  11. Optimization
  12. Experiments
  13. Experimental Settings
  14. Main Results
  15. Ablation Study
  16. ...and 17 more sections

Key Result

theorem 1

Suppose random vector $\mathcal{W} = (\mathcal{X},\mathcal{M},\mathcal{E},\mathcal{F},\mathcal{Y})$ follows a causal structure defined by the Bayesian network in Figure fig:sup_vci. Then $\log p(\mathcal{Y}=\mathcal{Y'}|\mathcal{M})$ has the following variational lower bound:

Figures (7)

  • Figure 1: An illustration of the causal chain in human sarcasm understanding, contrasted with different paradigms.
  • Figure 2: The overall architecture of MuVaC comprises five key steps: (a) Multimodal Feature Extraction, (b) Multimodal Feature Fusion, (c) Causal Explanation Generation, (d) Causal Feature Extraction, and (e) Causal-Informed Sarcasm Detection.
  • Figure 3: Structural causal models for MSD and MuSE.
  • Figure 4: Causal results of manually intervening $\mathcal{E}$ and $\mathcal{F}$.
  • Figure 5: Case study on MUStARD and WITS datasets.
  • ...and 2 more figures

Theorems & Definitions (1)

  • theorem 1