arXiv:2402.18107v1 Announce Type: new
Abstract: In line with the latest research, the task of identifying helpful reviews from a vast pool of user-generated textual and visual data has become a prominent area of study. Effective modal representations are expected to possess two key attributes: consistency and differentiation. Current methods designed for Multimodal Review Helpfulness Prediction (MRHP) face limitations in capturing distinctive information due to their reliance on uniform multimodal annotation. The process of adding varied multimodal annotations is not only time-consuming but also labor-intensive. To tackle these challenges, we propose an auto-generated scheme based on multi-task learning to generate pseudo labels. This approach allows us to simultaneously train for the global multimodal interaction task and the separate cross-modal interaction subtasks, enabling us to learn and leverage both consistency and differentiation effectively. Subsequently, experimental results validate the effectiveness of pseudo labels, and our approach surpasses previous textual and multimodal baseline models on two widely accessible benchmark datasets, providing a solution to the MRHP problem.
Expert Commentary: Enhancing Multimodal Review Helpfulness Prediction Using Pseudo Labels
With the rapid growth of user-generated content, identifying helpful reviews from a vast pool of textual and visual data has become a challenging task. In this research paper, the authors address the limitations of current methods for Multimodal Review Helpfulness Prediction (MRHP) by proposing a novel approach based on multi-task learning and pseudo labels.
The authors highlight two key attributes that effective modal representations should possess: consistency and differentiation. Consistency ensures that the multimodal annotations capture reliable and recurring information, while differentiation allows for the identification of unique and diverse aspects of the reviews.
One major limitation in existing methods is the reliance on uniform multimodal annotation, which fails to capture distinctive information. Moreover, the process of adding varied annotations manually is time-consuming and labor-intensive. To overcome these challenges, the authors introduce an auto-generated scheme based on multi-task learning.
The proposed approach leverages pseudo labels, which are automatically generated during training. This enables the model to simultaneously learn the global multimodal interaction task and the separate cross-modal interaction subtasks, effectively capturing both consistency and differentiation in the data.
The experiments conducted by the authors demonstrate the effectiveness of the pseudo labels and the proposed approach. The results show that the method outperforms previous textual and multimodal baseline models on two widely accessible benchmark datasets, offering a solution to the MRHP problem.
This research contributes to the field of multimedia information systems by addressing the challenges of identifying helpful reviews from multimodal data. By incorporating both textual and visual information, the proposed approach takes into account the multi-disciplinary nature of the content. This is particularly relevant in the context of multimedia information systems, where different modalities such as text, images, and videos need to be analyzed and interpreted.
The concepts presented in this paper also have implications for other related fields such as animations, artificial reality, augmented reality, and virtual realities. In these domains, the ability to accurately assess user-generated content and determine its helpfulness can greatly enhance user experiences. For example, in virtual reality applications, knowing which reviews provide valuable insights can assist developers in improving their virtual environments or applications.
In summary, this research paper provides a valuable contribution to the field of multimodal review analysis by proposing a novel approach based on pseudo labels and multi-task learning. By addressing the limitations of current methods and leveraging both consistency and differentiation, the proposed approach offers a promising solution to the MRHP problem. The findings of this study have implications for a wide range of domains, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.