Source-linked AI summary
Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement
Hui Liu, Wenya Wang, Haoliang Li
TL;DR
Sarcasm detection remains difficult because text can conceal discrepancies between literal meaning and intended meaning, while prior multimodal methods often overlook compositional alignments and external knowledge. The paper proposes hierarchical atomic- and composition-level congruity modeling with image-caption enhancement. On a public Twitter-based benchmark, the model outperforms state-of-the-art methods, and image captions further improve performance.
Problem
Existing multimodal sarcasm detectors mainly model token–image-patch inconsistencies while neglecting compositional structure and rich external knowledge such as image captions.
Method
The framework combines multi-head cross attention for atomic-level congruity, graph attention networks for composition-level congruity, and foundation-model-generated image captions as external knowledge.
Results
The model outperforms state-of-the-art methods, with image captions providing additional performance improvement on the public Twitter-based benchmark.
Takeaways & Limitations
Hierarchical cross-modal congruity modeling and image captions provide useful signals for multimodal sarcasm detection.
Takeaways & Limitations
Evaluation uses the only known benchmark for multimodal sarcasm detection, and the knowledge-enhancement strategy may not suit adjective-noun pairs and image attributes.
Abstract
from arXiv · showhide
Sarcasm is a linguistic phenomenon indicating a discrepancy between literal meanings and implied intentions. Due to its sophisticated nature, it is usually challenging to be detected from the text itself. As a result, multi-modal sarcasm detection has received more attention in both academia and industries. However, most existing techniques only modeled the atomic-level inconsistencies between the text input and its accompanying image, ignoring more complex compositions for both modalities. Moreover, they neglected the rich information contained in external knowledge, e.g., image captions. In this paper, we propose a novel hierarchical framework for sarcasm detection by exploring both the atomic-level congruity based on multi-head cross attention mechanism and the composition-level congruity based on graph neural networks, where a post with low congruity can be identified as sarcasm. In addition, we exploit the effect of various knowledge resources for sarcasm detection. Evaluation results on a public multi-modal sarcasm detection dataset based on Twitter demonstrate the superiority of our proposed model.
1 Introduction
Sarcasm detection is difficult because literal wording can diverge from speaker intent, and text alone may miss contradictions revealed by images. The paper addresses these gaps with hierarchical cross-modal congruity modeling and image-derived external knowledge.
- Motivation: Sarcasm contrasts literal wording with the speaker’s authentic intention, making detection difficult because of figurative language and intricate synonymy.The issue is especially relevant on social platforms such as Twitter and Reddit.
- Motivation: Text-only methods can miss sarcasm when the accompanying image reveals the utterance’s actual sentiment.In the example, a request for good news conflicts with an image showing severe storms.
- Research gap: Existing multimodal methods often model token–image-patch congruity while overlooking multi-granularity and composition-level inconsistencies.These inconsistencies can involve related entities or groups of image patches rather than isolated elements.
- Research gap: External world knowledge can support sarcasm identification, but image attributes and adjective-noun pairs may be insufficient or inaccurate because of limited training data.The paper therefore investigates how different external knowledge resources should be selected and integrated.
- Approach: The proposed framework jointly models atomic-level and composition-level congruity between textual and visual modalities.It uses multi-head cross attention for atomic interactions and graph attention networks for composition-level representations.
- Approach: The paper introduces image captions generated by transferable foundation models as external knowledge for hierarchical multimodal sarcasm detection.The generated captions provide additional image information and can be compared with the original text at multiple granularities.
2 Related Work
Multimodal sarcasm detection emerged as text-and-image posts became common, building on datasets and models that combine visual and textual signals. Related work also incorporated image attributes and adjective-noun pairs, while this paper uses descriptive captions generated from images.
- Multimodal sarcasm detection: Multimodal sarcasm detection gained attention as multimodal social-media posts became increasingly common.The task was defined by prior work, followed by a Twitter dataset and baseline that fused textual and visual features.
- Multimodal sarcasm detection: Prior models captured cross-modal contrast and semantic associations through decomposed and relational representations.These approaches extended sarcasm detection beyond isolated textual features.
- External knowledge: Commonsense knowledge has been identified as important for sarcasm detection, motivating image attributes and adjective-noun pairs as additional visual semantics.These resources were extracted from images and used as a third modality or semantic bridge.
- External knowledge: This paper generates descriptive image captions with ClipCap, which combines CLIP image encoding with a mapping network and GPT-2 caption generation.The captions are intended to provide richer semantic information than short image-derived resources.
3 Methodology
The framework detects multimodal sarcasm by combining atomic token–image-patch congruity, composition-level graph-based congruity, and optional external knowledge. It represents textual and visual structures, aligns modalities with cross attention, and uses generated or extracted knowledge as an additional textual modality.
- Framework overview: The model includes feature extraction, atomic-level cross-modal congruity, composition-level cross-modal congruity, and knowledge enhancement.Text and image encoders produce inputs for the congruity modules, while knowledge can be incorporated when available.
- Composition-Level Cross-Modal Congruity: Composition-level modeling captures complex inconsistencies that cannot be represented by isolated tokens or image patches.The approach compares composed textual representations with image representations after graph-based propagation.
- Atomic-Level Cross-Modal Congruity: Multi-head cross attention aligns textual and visual features before computing token–image-patch similarity scores for atomic-level congruity.The method uses cross attention to reduce the gap between modalities and then applies token-importance weighting to obtain patch-level congruity.
- Atomic-Level Cross-Modal Congruity: The visual modality is used as the query in cross attention only empirically less effectively than using text as the query.The authors conjecture that visual features may be less expressive and provide insufficient attentive guidance.
- Composition-Level Cross-Modal Congruity: Textual and visual graphs model composition-level congruity by propagating atomic representations across dependency and neighborhood relations with graph attention networks.Text nodes use dependency relations, while GAT layers incorporate complex dependencies into composition-level textual and visual embeddings.
- Knowledge Enhancement: The model can treat external knowledge as a virtual textual modality and compute additional text–knowledge congruity after text–image alignment.Knowledge representations are aligned with updated text representations, and knowledge graphs can provide composition-level congruity scores.
4 Experiments
Experiments on a public English Twitter-based multi-modal sarcasm dataset show that the proposed framework achieves state-of-the-art performance. Results indicate benefits from combining modalities, hierarchical congruity modeling, and image-caption knowledge, while component depth affects performance.
- Baseline comparison: The proposed model achieves state-of-the-art performance against text-, image-, and multi-modal sarcasm detection baselines.The evaluation uses a publicly available multi-modal sarcasm detection benchmark constructed from Twitter data.
- Baseline comparison: Multi-modal methods outperform single-modality models, while text-based models outperform image-only methods on the benchmark.The authors interpret this as evidence that combining modalities provides additional modality-association cues and that text is more informative than images alone.
- Baseline comparison: 0.97% Accuracy and 1.00% F1-score improvements over Att-BERT are reported with ResNet, while ViT yields 1.26% and 1.25% gains over InCrossMGs.The reported improvements are respectively measured in Accuracy and F1-score against the named multi-modal baselines.
- External knowledge: Image captions improve performance over the model without external knowledge, whereas image attributes and ANPs reduce performance.The authors conjecture that attributes and ANPs may be noisy or too short to provide the compositional information required by the hierarchical model.
- Ablation study: The full model performs best when combining atomic-level congruity, multi-head cross attention, and composition-level congruity.Removing composition-level congruity causes a larger performance drop than removing atomic-level congruity, while removing cross attention causes a smaller decrease.
- Architecture analysis: Performance peaks with six MCA layers and two GAT layers, then declines with additional layers; CLIP sentence embeddings slightly outperform alternatives, but Word Averaging is preferred for concision.The authors associate the later declines with overfitting for MCA layers and over-smoothing for deeper graph propagation.
5 Conclusion
The paper proposes hierarchical sarcasm detection through atomic-level and composition-level congruity modeling, while also evaluating external knowledge resources. Results demonstrate model superiority and a benefit from image captions as external knowledge.
- The framework reasons about atomic-level and composition-level congruity hierarchically for multimodal sarcasm detection.
- The study evaluates various knowledge resources to enhance the model’s discriminative power.
- Evaluation results demonstrate the model’s superiority and the benefit of image captions as external knowledge for sarcasm detection.
Limitations
The paper identifies limitations concerning evaluation coverage and knowledge integration. It uses only a Twitter dataset and notes that its knowledge enhancement strategy may not suit ANPs and Image Attributes.
- Evaluation uses only the Twitter dataset, limiting benchmark coverage despite its status as the only known community benchmark.
- The paper leaves construction of more high-quality benchmarks for future work.
- The knowledge enhancement strategy may not be suitable for ANPs and Image Attributes.
- The paper calls for a more general and elegant knowledge integration method for multimodal sarcasm detection.
Ethics Statement
The paper states that it follows the ACM Code of Ethics and Professional Conduct in its research practices and artifact handling.
- The authors respect prior research and cite relevant papers, pretrained models, and toolkits used in the work.
- The authors state that released code will follow the licenses of used artifacts.
- The adopted dataset is stated not to include sensitive individual privacy information.
A Model Overview
The text-image branch contains feature representation, atomic-level cross-modality congruity, and composition-level cross-modality congruity modules. It computes congruity at token or object level and through constructed textual and visual graphs.
- Feature Representation extracts representations corresponding to the text and image modalities.
- The text-image branch comprises Feature Representation, Atomic-Level Cross Modality Congruity, and Composition-Level Cross Modality Congruity.
- Atomic-Level Cross Modality Congruity obtains congruity scores through multi-head cross-attention.
- Composition-Level Cross Modality Congruity produces scores from constructed textual and visual graphs.
B Modal Parameters
The model uses fixed sequence lengths and specified attention and graph-layer configurations to compute atomic- and composition-level congruity scores. Training uses Adam with defined optimization and regularization settings.
- Sarcasm text is limited to 100 tokens and generated image captions to 20 tokens.
- The text-image branch uses 6 multi-head cross-attention layers, while the text-knowledge branch uses 3 layers.Both branches use 5 attention heads to capture atomic-level congruity.
- Two graph-attention layers obtain composition-level congruity scores for both branches.
- The model is trained with Adam at a 2e−5 learning rate, 5e−3 weight decay, batch size 32, and dropout rate 0.5.