Detecting Persuasion Techniques in Multimodal Memes: A Multilingual Exploration

Temel Kavramlar

The core message of this paper is to develop models that can effectively detect rhetorical and psychological persuasion techniques embedded within memes, leveraging both textual and visual modalities. The authors introduce an intermediate step of generating meme captions to bridge the gap between the textual and visual components, which improves the performance of their models.

Özet

The paper discusses the authors' participation in the SemEval-2024 shared task 4, which focuses on developing models to detect persuasion techniques in memes. The task involves three subtasks:

Subtask 1: Identifying persuasion techniques in the textual content of memes.
Subtask 2a: Identifying persuasion techniques using both the textual and visual content of memes.
Subtask 2b: A binary classification version of Subtask 2a.

The authors introduce an intermediate step of generating meme captions using language models like LLaVA-1.5 and GPT-4. They then use these captions, along with the meme text, to fine-tune various models, including LLMs, MLLMs, and LRMs, for the classification tasks.
The results show that the models incorporating the generated captions perform better than those using only the meme text or image. The authors attribute this improvement to the ability of language models to better capture the metaphorical and semantic aspects of the memes, which may not be fully conveyed by the visual encoders.
The authors also explore the performance of their models on non-English memes (Arabic, Bulgarian, and North Macedonian) in a zero-shot setting, demonstrating the potential for their approach to be applied to low-resource languages.

İstatistikler

The training set released for all subtasks contains only English memes.
The test datasets contain memes in three low-resource languages (Arabic, Bulgarian, and North Macedonian) in addition to English.

Alıntılar

"Memes, combining text and images, frequently use metaphors to convey persuasive messages, shaping public opinion."
"To tackle this problem, we introduced a caption generation step to assess the modality gap and the impact of additional semantic information from images, which improved our result."
"Our best model utilizes GPT-4 generated captions alongside meme text to fine-tune RoBERTa as the text encoder and CLIP as the image encoder."

Önemli Bilgiler Şuradan Elde Edildi

BCAmirs at SemEval-2024 Task 4

by Amirhossein ... : arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03022.pdf

Daha Derin Sorular

How can the proposed approach be further improved to better capture the metaphorical and cultural nuances in memes, especially for low-resource languages?

To better capture the metaphorical and cultural nuances in memes, especially for low-resource languages, the proposed approach can be further improved in the following ways:

Fine-tuning for Cultural Sensitivity: Incorporating a cultural sensitivity aspect during the training of the models can help them better understand and interpret the cultural references and nuances present in memes. This can involve training the models on diverse cultural datasets to enhance their understanding of different cultural contexts.

Multilingual Training Data: Including more multilingual training data, especially from low-resource languages, can help the models learn a wider range of cultural and linguistic nuances. This exposure to diverse language patterns and cultural references can improve the models' ability to capture the subtleties in memes across different languages.

Ensemble Models: Utilizing ensemble models that combine the strengths of multiple models trained on different aspects of memes, such as text, images, and captions, can provide a more comprehensive understanding of the content. By aggregating the outputs of these models, the system can capture a broader range of metaphorical and cultural nuances.

Human-in-the-Loop Validation: Implementing a human-in-the-loop validation system where human annotators review and provide feedback on the model-generated captions can help refine the model's understanding of metaphorical and cultural nuances. This iterative process can enhance the model's performance over time.

Domain-Specific Training: Tailoring the training data to include specific meme categories or themes prevalent in low-resource languages can help the models better grasp the unique cultural references and metaphors used in those memes. This domain-specific training can improve the models' performance in capturing nuanced content.

How can the proposed approach be further improved to better capture the metaphorical and cultural nuances in memes, especially for low-resource languages?

To better capture the metaphorical and cultural nuances in memes, especially for low-resource languages, the proposed approach can be further improved in the following ways:

Fine-tuning for Cultural Sensitivity: Incorporating a cultural sensitivity aspect during the training of the models can help them better understand and interpret the cultural references and nuances present in memes. This can involve training the models on diverse cultural datasets to enhance their understanding of different cultural contexts.

Multilingual Training Data: Including more multilingual training data, especially from low-resource languages, can help the models learn a wider range of cultural and linguistic nuances. This exposure to diverse language patterns and cultural references can improve the models' ability to capture the subtleties in memes across different languages.

Ensemble Models: Utilizing ensemble models that combine the strengths of multiple models trained on different aspects of memes, such as text, images, and captions, can provide a more comprehensive understanding of the content. By aggregating the outputs of these models, the system can capture a broader range of metaphorical and cultural nuances.

Human-in-the-Loop Validation: Implementing a human-in-the-loop validation system where human annotators review and provide feedback on the model-generated captions can help refine the model's understanding of metaphorical and cultural nuances. This iterative process can enhance the model's performance over time.

Domain-Specific Training: Tailoring the training data to include specific meme categories or themes prevalent in low-resource languages can help the models better grasp the unique cultural references and metaphors used in those memes. This domain-specific training can improve the models' performance in capturing nuanced content.

How can the proposed approach be further improved to better capture the metaphorical and cultural nuances in memes, especially for low-resource languages?

To better capture the metaphorical and cultural nuances in memes, especially for low-resource languages, the proposed approach can be further improved in the following ways:

Fine-tuning for Cultural Sensitivity: Incorporating a cultural sensitivity aspect during the training of the models can help them better understand and interpret the cultural references and nuances present in memes. This can involve training the models on diverse cultural datasets to enhance their understanding of different cultural contexts.

Multilingual Training Data: Including more multilingual training data, especially from low-resource languages, can help the models learn a wider range of cultural and linguistic nuances. This exposure to diverse language patterns and cultural references can improve the models' ability to capture the subtleties in memes across different languages.

Ensemble Models: Utilizing ensemble models that combine the strengths of multiple models trained on different aspects of memes, such as text, images, and captions, can provide a more comprehensive understanding of the content. By aggregating the outputs of these models, the system can capture a broader range of metaphorical and cultural nuances.

Human-in-the-Loop Validation: Implementing a human-in-the-loop validation system where human annotators review and provide feedback on the model-generated captions can help refine the model's understanding of metaphorical and cultural nuances. This iterative process can enhance the model's performance over time.

Domain-Specific Training: Tailoring the training data to include specific meme categories or themes prevalent in low-resource languages can help the models better grasp the unique cultural references and metaphors used in those memes. This domain-specific training can improve the models' performance in capturing nuanced content.

How can the proposed approach be further improved to better capture the metaphorical and cultural nuances in memes, especially for low-resource languages?

To better capture the metaphorical and cultural nuances in memes, especially for low-resource languages, the proposed approach can be further enhanced through the following strategies:

Fine-tuning with Diverse Cultural Data: Training the models on a more diverse set of cultural data, including specific datasets from low-resource languages, can help improve their understanding of cultural references and nuances in memes. This exposure can enhance the models' ability to interpret metaphorical content accurately.

Cross-Lingual Transfer Learning: Implementing cross-lingual transfer learning techniques can enable the models to leverage knowledge from high-resource languages to better understand and generate captions for memes in low-resource languages. This transfer of knowledge can bridge the gap in capturing cultural nuances.

Contextual Embeddings: Incorporating contextual embeddings that capture the cultural context of memes can enhance the models' ability to interpret metaphorical and cultural nuances. By encoding cultural information into the embeddings, the models can better grasp the underlying meanings in memes.

Adversarial Training: Introducing adversarial training techniques can help the models become more robust against adversarial attacks that aim to distort the cultural and metaphorical meanings in memes. By exposing the models to diverse adversarial examples, they can learn to better discern genuine cultural nuances.

Human Annotation: Integrating human annotation in the training process can provide valuable insights into the cultural and metaphorical aspects of memes. Human annotators can help identify and correct misinterpretations by the models, improving their ability to capture nuanced content accurately.