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.

翻译：与最新研究一致，从海量用户生成的文本和视觉数据中识别有用评论已成为一个突出研究领域。有效的模态表征应具备两个关键属性：一致性和差异性。当前针对多模态评论有用性预测（MRHP）的方法由于依赖统一的模态标注，在捕捉区分性信息方面存在局限。添加多样化模态标注的过程不仅耗时且劳动密集。为解决这些挑战，我们提出一种基于多任务学习的自动生成方案来生成伪标签。该方法使我们能够同时训练全局多模态交互任务与分离的跨模态交互子任务，从而有效学习并利用一致性与差异性。随后，实验结果验证了伪标签的有效性，本方法在两个广泛可用的基准数据集上超越了先前的文本及多模态基线模型，为多模态评论有用性预测问题提供了解决方案。