  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.
