InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection

Junjie Chen, Subin Huang·June 24, 2024

Summary

The paper introduces InterCLIP-MEP, a state-of-the-art framework for multi-modal sarcasm detection that enhances the CLIP model by refining its interaction encoding and incorporating a Memory-Enhanced Predictor (MEP) to store historical knowledge. The model addresses the challenges in understanding text-image combinations by capturing the interplay between the two modalities. InterCLIP-MEP outperforms existing methods on the MMSD2.0 benchmark, demonstrating its effectiveness in capturing sarcasm. The framework combines interactive text-image encoding, memory-based predictions, and various training strategies, including conditional self-attention and LoRA fine-tuning. Ablation studies highlight the importance of different components and interaction modes, with InterCLIP-MEP w/ T2V showing the best performance. Future work involves expanding the approach to other modalities. The research contributes to the ongoing development of multi-modal sentiment analysis and sarcasm detection in online content.

Key findings

5

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of biases from spurious cues in Multi-modal Sarcasm Detection (MMSD) by proposing a robust framework called InterCLIP-MEP. This framework introduces Interactive CLIP (InterCLIP) for enhanced sample representations by embedding cross-modality information and a Memory-Enhanced Predictor (MEP) for leveraging historical knowledge to improve inference . The issue of biases from spurious cues in MMSD is not new and has been recognized in previous research . The paper's contribution lies in offering a refined solution to this existing problem in multi-modal sarcasm detection.


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that Multi-Modal Sarcasm Detection (MMSD) methods may contain spurious cues that could introduce biases into models . The study explores the need to address these biases by refining benchmarks, such as the introduction of MMSD2.0, which eliminates spurious cues and corrects mislabeled samples . The research focuses on enhancing sarcasm detection performance by improving the accuracy of identifying sarcasm cues in multi-modal communications .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection" introduces innovative ideas, methods, and models for enhancing multi-modal sarcasm detection . The key contributions of the paper include:

  1. Interactive CLIP (InterCLIP): The paper introduces InterCLIP, a refined variant of CLIP, which enhances sample encoding by embedding representations from one modality into the encoder of the other modality. This approach enables a more nuanced understanding of the interaction between text and image, crucial for multi-modal sarcasm detection .

  2. Memory-Enhanced Prediction (MEP): The framework incorporates a novel training strategy that integrates a classification module and a projection module, with InterCLIP as the backbone. These modules are designed to identify sarcastic samples while constructing a latent space where features of the same class are clustered together. This memory-enhanced prediction mechanism aids in improving the reliability of sarcasm detection .

  3. MMSD2.0 Benchmark: The paper addresses the limitations of existing methods by introducing the MMSD2.0 benchmark, which eliminates spurious cues and corrects mislabeled samples. This refined benchmark aims to enhance the reliability of multi-modal sarcasm detection by providing a more stable evaluation platform .

  4. Comparison with Existing Methods: The paper compares the effectiveness of the InterCLIP-MEP framework with several uni-modal and multi-modal methods commonly used in multi-modal sarcasm detection. By leveraging Interactive CLIP and Memory-Enhanced Prediction, the paper demonstrates improved performance in detecting sarcastic samples across different modalities .

In summary, the paper proposes a comprehensive framework that leverages Interactive CLIP for enhanced sample encoding, Memory-Enhanced Prediction for reliable detection, and introduces the MMSD2.0 benchmark to address the limitations of existing methods in multi-modal sarcasm detection . The "InterCLIP-MEP" framework introduces several key characteristics and advantages compared to previous methods in multi-modal sarcasm detection :

  1. Interactive CLIP (InterCLIP): One of the main features of the InterCLIP-MEP framework is the utilization of Interactive CLIP, which enhances sample encoding by embedding representations from one modality into the encoder of the other modality. This approach enables a more comprehensive understanding of the interaction between text and image, leading to improved sarcasm detection across different modalities .

  2. Memory-Enhanced Prediction (MEP): The framework incorporates a novel training strategy that integrates a classification module and a projection module, with InterCLIP as the backbone. By constructing a latent space where features of the same class are clustered together, the MEP mechanism enhances the reliability of sarcasm detection .

  3. MMSD2.0 Benchmark: The paper addresses the limitations of existing methods by introducing the MMSD2.0 benchmark, which eliminates spurious cues and corrects mislabeled samples. This refined benchmark aims to enhance the reliability of multi-modal sarcasm detection by providing a more stable evaluation platform .

  4. Comparison with Existing Methods: The InterCLIP-MEP framework is compared with several uni-modal and multi-modal methods commonly used in multi-modal sarcasm detection. By leveraging Interactive CLIP and Memory-Enhanced Prediction, the paper demonstrates improved performance in detecting sarcastic samples across different modalities .

  5. Hyperparameter Study: The framework conducts an in-depth hyperparameter study to optimize model performance. Fine-tuning different weight matrices and exploring various hyperparameters such as LoRA ranks, memory sizes, and projection dimensions contribute to the effectiveness of the InterCLIP-MEP framework .

In summary, the InterCLIP-MEP framework stands out due to its innovative use of Interactive CLIP, Memory-Enhanced Prediction, the introduction of the MMSD2.0 benchmark, and the thorough hyperparameter study, all of which contribute to its enhanced performance in multi-modal sarcasm detection compared to previous methods.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of multi-modal sarcasm detection. Noteworthy researchers in this field include Liu, Wang, and Li , Qin et al. , Wen, Jia, and Yang , Tian et al. , Wei et al. , Xu, Zeng, and Mao , Pan et al. , Liang et al. , and many others who have contributed to advancements in multi-modal sarcasm detection .

The key to the solution mentioned in the paper involves the development of the InterCLIP-MEP framework, which integrates Interactive CLIP and Memory-Enhanced Predictor for multi-modal sarcasm detection. This framework utilizes Transformer-based models for high-quality representation extraction, OCR and object detection technologies for image understanding, and incorporates external emotional knowledge. It also employs graph neural networks to construct dependency and cross-modal graphs for improved sarcasm identification .


How were the experiments in the paper designed?

The experiments in the paper were designed by conducting experiments using different configurations and interaction modes of the InterCLIP-MEP framework to validate its effectiveness . The experiments involved comparing the performance of the framework with the original CLIP as the backbone and different interaction modes of InterCLIP, such as InterCLIP-MEP w/ V2T, InterCLIP-MEP w/ T2V, and InterCLIP-MEP w/ TW . Each experiment conditioned only the top four layers of the self-attention modules, with specific hyperparameters set, such as the projection dimension and LoRA rank, to evaluate the framework's performance . Additionally, an ablation study was conducted to further validate the effectiveness of InterCLIP-MEP by removing specific modules like the projection module and fine-tuning LoRA to assess their impact on the model's performance . The experiments aimed to demonstrate the superiority of the InterCLIP-MEP framework for multi-modal sarcasm detection by leveraging interactive cross-modal encoding and memory-enhanced prediction .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the MMSD2.0 benchmark . The code for the framework, InterCLIP-MEP, is not explicitly mentioned to be open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study introduces the InterCLIP-MEP framework for multi-modal sarcasm detection, combining interactive cross-modal encoding with memory-enhanced prediction . The framework leverages a variant of CLIP called Interactive CLIP (InterCLIP) to enhance the understanding of text-image interactions and incorporates a novel training strategy integrating classification and projection modules for sarcasm identification . The results of the experiments demonstrate the effectiveness of the InterCLIP-MEP framework in improving multi-modal sarcasm detection performance .

The study conducted ablation studies to validate the effectiveness of the InterCLIP-MEP framework by analyzing different variants of the model . The results consistently showed that all variants, when compared to the baseline, exhibited performance declines, highlighting the importance of the components such as the projection module, LoRA fine-tuning, and memory-enhanced prediction in achieving optimal results . This thorough analysis of different model configurations provides valuable insights into the impact of each component on the overall performance of the framework.

Furthermore, the hyperparameter study conducted in the paper investigated the impact of different hyperparameter settings on model performance . The study revealed that fine-tuning specific weight matrices and utilizing the T2V interaction mode of Interactive-CLIP as the backbone were crucial for the effectiveness of the InterCLIP-MEP framework . Additionally, the study explored the influence of different projection dimensions, LoRA ranks, and memory sizes on the model's performance, providing a comprehensive analysis of the hyperparameters' impact on the framework's efficacy .

In conclusion, the experiments, ablation studies, and hyperparameter analyses conducted in the paper collectively provide robust support for the scientific hypotheses underlying the development and validation of the InterCLIP-MEP framework for multi-modal sarcasm detection. The results obtained from these analyses contribute significantly to the understanding of the framework's performance and its ability to enhance multi-modal sarcasm detection accuracy.


What are the contributions of this paper?

The paper makes several significant contributions in the field of multi-modal sarcasm detection:

  • It introduces InterCLIP-MEP, an interactive CLIP and Memory-Enhanced Predictor for multi-modal sarcasm detection, which achieves high accuracy and F1 scores .
  • The research expands on Multi-Modal Sarcasm Detection (MMSD) by exploring dependencies, external knowledge, advanced models, and graph-based methods to enhance performance .
  • The paper addresses the limitations of existing methods by introducing MMSD2.0, a refined benchmark that eliminates biases from spurious cues and corrects mislabeled samples .
  • Various approaches are explored, including the use of Transformer-based models, OCR, object detection technologies for image understanding, and the incorporation of external emotional knowledge .
  • The study utilizes graph neural networks to construct dependency and cross-modal graphs for improved sarcasm identification .
  • The paper highlights the importance of refining benchmarks and addressing biases in multi-modal sarcasm detection systems .

What work can be continued in depth?

To further advance research in multi-modal sarcasm detection, several areas can be explored in depth based on the existing work:

  • Exploring Dependencies and External Knowledge: Future research can delve deeper into exploring dependencies and external knowledge to enhance performance in multi-modal sarcasm detection .
  • Utilizing Graph-Based Methods: Investigating the use of graph-based methods, such as graph neural networks, to construct dependency and cross-modal graphs for improved sarcasm identification can be a promising direction for further study .
  • Refining Benchmarking: Continuation of work on refining benchmarks, like the introduction of MMSD2.0, to eliminate spurious cues and correct mislabeled samples can contribute to more reliable and robust multi-modal sarcasm detection systems .

Introduction
Background
Evolution of multi-modal sarcasm detection
Challenges in understanding text-image combinations
Objective
To develop a robust framework for sarcasm detection
Improve upon existing methods and address modality interaction
Method
Interactive Text-Image Encoding
CLIP Model Refinement
Enhancing CLIP with interaction encoding
Text-Image Interplay Capture
Conditional self-attention mechanism
Memory-Enhanced Predictor (MEP)
Design and implementation
Historical knowledge storage and retrieval
Data Collection
MMSD2.0 benchmark dataset
Multi-modal data preprocessing
Data Preprocessing
Text and image feature extraction
Alignment and fusion techniques
Training Strategies
LoRA fine-tuning for model optimization
Ablation studies on component importance
Model Variants
InterCLIP-MEP w/ T2V: Best-performing model
Results and Evaluation
Performance on MMSD2.0 benchmark
Comparison with state-of-the-art methods
Ablation Studies
Contribution of different components to overall performance
Future Work
Expansion to other modalities
Potential applications in online content analysis
Conclusion
Contribution to multi-modal sentiment analysis
Implications for sarcasm detection in real-world scenarios
References
Cited works in the field of multi-modal sarcasm detection and related research
Basic info
papers
computation and language
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
What is the primary focus of InterCLIP-MEP?
How does InterCLIP-MEP improve upon the CLIP model for sarcasm detection?
What are the key components and training strategies employed in the InterCLIP-MEP framework?
Which benchmark does InterCLIP-MEP excel in, and what is its significance?

InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection

Junjie Chen, Subin Huang·June 24, 2024

Summary

The paper introduces InterCLIP-MEP, a state-of-the-art framework for multi-modal sarcasm detection that enhances the CLIP model by refining its interaction encoding and incorporating a Memory-Enhanced Predictor (MEP) to store historical knowledge. The model addresses the challenges in understanding text-image combinations by capturing the interplay between the two modalities. InterCLIP-MEP outperforms existing methods on the MMSD2.0 benchmark, demonstrating its effectiveness in capturing sarcasm. The framework combines interactive text-image encoding, memory-based predictions, and various training strategies, including conditional self-attention and LoRA fine-tuning. Ablation studies highlight the importance of different components and interaction modes, with InterCLIP-MEP w/ T2V showing the best performance. Future work involves expanding the approach to other modalities. The research contributes to the ongoing development of multi-modal sentiment analysis and sarcasm detection in online content.
Mind map
InterCLIP-MEP w/ T2V: Best-performing model
Alignment and fusion techniques
Text and image feature extraction
Multi-modal data preprocessing
MMSD2.0 benchmark dataset
Conditional self-attention mechanism
Enhancing CLIP with interaction encoding
Contribution of different components to overall performance
Model Variants
Data Preprocessing
Data Collection
Text-Image Interplay Capture
CLIP Model Refinement
Improve upon existing methods and address modality interaction
To develop a robust framework for sarcasm detection
Challenges in understanding text-image combinations
Evolution of multi-modal sarcasm detection
Cited works in the field of multi-modal sarcasm detection and related research
Implications for sarcasm detection in real-world scenarios
Contribution to multi-modal sentiment analysis
Potential applications in online content analysis
Expansion to other modalities
Ablation Studies
Training Strategies
Memory-Enhanced Predictor (MEP)
Interactive Text-Image Encoding
Objective
Background
References
Conclusion
Future Work
Results and Evaluation
Method
Introduction
Outline
Introduction
Background
Evolution of multi-modal sarcasm detection
Challenges in understanding text-image combinations
Objective
To develop a robust framework for sarcasm detection
Improve upon existing methods and address modality interaction
Method
Interactive Text-Image Encoding
CLIP Model Refinement
Enhancing CLIP with interaction encoding
Text-Image Interplay Capture
Conditional self-attention mechanism
Memory-Enhanced Predictor (MEP)
Design and implementation
Historical knowledge storage and retrieval
Data Collection
MMSD2.0 benchmark dataset
Multi-modal data preprocessing
Data Preprocessing
Text and image feature extraction
Alignment and fusion techniques
Training Strategies
LoRA fine-tuning for model optimization
Ablation studies on component importance
Model Variants
InterCLIP-MEP w/ T2V: Best-performing model
Results and Evaluation
Performance on MMSD2.0 benchmark
Comparison with state-of-the-art methods
Ablation Studies
Contribution of different components to overall performance
Future Work
Expansion to other modalities
Potential applications in online content analysis
Conclusion
Contribution to multi-modal sentiment analysis
Implications for sarcasm detection in real-world scenarios
References
Cited works in the field of multi-modal sarcasm detection and related research
Key findings
5

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of biases from spurious cues in Multi-modal Sarcasm Detection (MMSD) by proposing a robust framework called InterCLIP-MEP. This framework introduces Interactive CLIP (InterCLIP) for enhanced sample representations by embedding cross-modality information and a Memory-Enhanced Predictor (MEP) for leveraging historical knowledge to improve inference . The issue of biases from spurious cues in MMSD is not new and has been recognized in previous research . The paper's contribution lies in offering a refined solution to this existing problem in multi-modal sarcasm detection.


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that Multi-Modal Sarcasm Detection (MMSD) methods may contain spurious cues that could introduce biases into models . The study explores the need to address these biases by refining benchmarks, such as the introduction of MMSD2.0, which eliminates spurious cues and corrects mislabeled samples . The research focuses on enhancing sarcasm detection performance by improving the accuracy of identifying sarcasm cues in multi-modal communications .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection" introduces innovative ideas, methods, and models for enhancing multi-modal sarcasm detection . The key contributions of the paper include:

  1. Interactive CLIP (InterCLIP): The paper introduces InterCLIP, a refined variant of CLIP, which enhances sample encoding by embedding representations from one modality into the encoder of the other modality. This approach enables a more nuanced understanding of the interaction between text and image, crucial for multi-modal sarcasm detection .

  2. Memory-Enhanced Prediction (MEP): The framework incorporates a novel training strategy that integrates a classification module and a projection module, with InterCLIP as the backbone. These modules are designed to identify sarcastic samples while constructing a latent space where features of the same class are clustered together. This memory-enhanced prediction mechanism aids in improving the reliability of sarcasm detection .

  3. MMSD2.0 Benchmark: The paper addresses the limitations of existing methods by introducing the MMSD2.0 benchmark, which eliminates spurious cues and corrects mislabeled samples. This refined benchmark aims to enhance the reliability of multi-modal sarcasm detection by providing a more stable evaluation platform .

  4. Comparison with Existing Methods: The paper compares the effectiveness of the InterCLIP-MEP framework with several uni-modal and multi-modal methods commonly used in multi-modal sarcasm detection. By leveraging Interactive CLIP and Memory-Enhanced Prediction, the paper demonstrates improved performance in detecting sarcastic samples across different modalities .

In summary, the paper proposes a comprehensive framework that leverages Interactive CLIP for enhanced sample encoding, Memory-Enhanced Prediction for reliable detection, and introduces the MMSD2.0 benchmark to address the limitations of existing methods in multi-modal sarcasm detection . The "InterCLIP-MEP" framework introduces several key characteristics and advantages compared to previous methods in multi-modal sarcasm detection :

  1. Interactive CLIP (InterCLIP): One of the main features of the InterCLIP-MEP framework is the utilization of Interactive CLIP, which enhances sample encoding by embedding representations from one modality into the encoder of the other modality. This approach enables a more comprehensive understanding of the interaction between text and image, leading to improved sarcasm detection across different modalities .

  2. Memory-Enhanced Prediction (MEP): The framework incorporates a novel training strategy that integrates a classification module and a projection module, with InterCLIP as the backbone. By constructing a latent space where features of the same class are clustered together, the MEP mechanism enhances the reliability of sarcasm detection .

  3. MMSD2.0 Benchmark: The paper addresses the limitations of existing methods by introducing the MMSD2.0 benchmark, which eliminates spurious cues and corrects mislabeled samples. This refined benchmark aims to enhance the reliability of multi-modal sarcasm detection by providing a more stable evaluation platform .

  4. Comparison with Existing Methods: The InterCLIP-MEP framework is compared with several uni-modal and multi-modal methods commonly used in multi-modal sarcasm detection. By leveraging Interactive CLIP and Memory-Enhanced Prediction, the paper demonstrates improved performance in detecting sarcastic samples across different modalities .

  5. Hyperparameter Study: The framework conducts an in-depth hyperparameter study to optimize model performance. Fine-tuning different weight matrices and exploring various hyperparameters such as LoRA ranks, memory sizes, and projection dimensions contribute to the effectiveness of the InterCLIP-MEP framework .

In summary, the InterCLIP-MEP framework stands out due to its innovative use of Interactive CLIP, Memory-Enhanced Prediction, the introduction of the MMSD2.0 benchmark, and the thorough hyperparameter study, all of which contribute to its enhanced performance in multi-modal sarcasm detection compared to previous methods.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of multi-modal sarcasm detection. Noteworthy researchers in this field include Liu, Wang, and Li , Qin et al. , Wen, Jia, and Yang , Tian et al. , Wei et al. , Xu, Zeng, and Mao , Pan et al. , Liang et al. , and many others who have contributed to advancements in multi-modal sarcasm detection .

The key to the solution mentioned in the paper involves the development of the InterCLIP-MEP framework, which integrates Interactive CLIP and Memory-Enhanced Predictor for multi-modal sarcasm detection. This framework utilizes Transformer-based models for high-quality representation extraction, OCR and object detection technologies for image understanding, and incorporates external emotional knowledge. It also employs graph neural networks to construct dependency and cross-modal graphs for improved sarcasm identification .


How were the experiments in the paper designed?

The experiments in the paper were designed by conducting experiments using different configurations and interaction modes of the InterCLIP-MEP framework to validate its effectiveness . The experiments involved comparing the performance of the framework with the original CLIP as the backbone and different interaction modes of InterCLIP, such as InterCLIP-MEP w/ V2T, InterCLIP-MEP w/ T2V, and InterCLIP-MEP w/ TW . Each experiment conditioned only the top four layers of the self-attention modules, with specific hyperparameters set, such as the projection dimension and LoRA rank, to evaluate the framework's performance . Additionally, an ablation study was conducted to further validate the effectiveness of InterCLIP-MEP by removing specific modules like the projection module and fine-tuning LoRA to assess their impact on the model's performance . The experiments aimed to demonstrate the superiority of the InterCLIP-MEP framework for multi-modal sarcasm detection by leveraging interactive cross-modal encoding and memory-enhanced prediction .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the MMSD2.0 benchmark . The code for the framework, InterCLIP-MEP, is not explicitly mentioned to be open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study introduces the InterCLIP-MEP framework for multi-modal sarcasm detection, combining interactive cross-modal encoding with memory-enhanced prediction . The framework leverages a variant of CLIP called Interactive CLIP (InterCLIP) to enhance the understanding of text-image interactions and incorporates a novel training strategy integrating classification and projection modules for sarcasm identification . The results of the experiments demonstrate the effectiveness of the InterCLIP-MEP framework in improving multi-modal sarcasm detection performance .

The study conducted ablation studies to validate the effectiveness of the InterCLIP-MEP framework by analyzing different variants of the model . The results consistently showed that all variants, when compared to the baseline, exhibited performance declines, highlighting the importance of the components such as the projection module, LoRA fine-tuning, and memory-enhanced prediction in achieving optimal results . This thorough analysis of different model configurations provides valuable insights into the impact of each component on the overall performance of the framework.

Furthermore, the hyperparameter study conducted in the paper investigated the impact of different hyperparameter settings on model performance . The study revealed that fine-tuning specific weight matrices and utilizing the T2V interaction mode of Interactive-CLIP as the backbone were crucial for the effectiveness of the InterCLIP-MEP framework . Additionally, the study explored the influence of different projection dimensions, LoRA ranks, and memory sizes on the model's performance, providing a comprehensive analysis of the hyperparameters' impact on the framework's efficacy .

In conclusion, the experiments, ablation studies, and hyperparameter analyses conducted in the paper collectively provide robust support for the scientific hypotheses underlying the development and validation of the InterCLIP-MEP framework for multi-modal sarcasm detection. The results obtained from these analyses contribute significantly to the understanding of the framework's performance and its ability to enhance multi-modal sarcasm detection accuracy.


What are the contributions of this paper?

The paper makes several significant contributions in the field of multi-modal sarcasm detection:

  • It introduces InterCLIP-MEP, an interactive CLIP and Memory-Enhanced Predictor for multi-modal sarcasm detection, which achieves high accuracy and F1 scores .
  • The research expands on Multi-Modal Sarcasm Detection (MMSD) by exploring dependencies, external knowledge, advanced models, and graph-based methods to enhance performance .
  • The paper addresses the limitations of existing methods by introducing MMSD2.0, a refined benchmark that eliminates biases from spurious cues and corrects mislabeled samples .
  • Various approaches are explored, including the use of Transformer-based models, OCR, object detection technologies for image understanding, and the incorporation of external emotional knowledge .
  • The study utilizes graph neural networks to construct dependency and cross-modal graphs for improved sarcasm identification .
  • The paper highlights the importance of refining benchmarks and addressing biases in multi-modal sarcasm detection systems .

What work can be continued in depth?

To further advance research in multi-modal sarcasm detection, several areas can be explored in depth based on the existing work:

  • Exploring Dependencies and External Knowledge: Future research can delve deeper into exploring dependencies and external knowledge to enhance performance in multi-modal sarcasm detection .
  • Utilizing Graph-Based Methods: Investigating the use of graph-based methods, such as graph neural networks, to construct dependency and cross-modal graphs for improved sarcasm identification can be a promising direction for further study .
  • Refining Benchmarking: Continuation of work on refining benchmarks, like the introduction of MMSD2.0, to eliminate spurious cues and correct mislabeled samples can contribute to more reliable and robust multi-modal sarcasm detection systems .
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