Computer Science > Multimedia
[Submitted on 28 Feb 2024 (v1), last revised 25 Mar 2024 (this version, v2)]
Title:Multimodal Interaction Modeling via Self-Supervised Multi-Task Learning for Review Helpfulness Prediction
View PDF HTML (experimental)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.
Submission history
From: Honglin Gong [view email][v1] Wed, 28 Feb 2024 06:54:35 UTC (4,165 KB)
[v2] Mon, 25 Mar 2024 05:28:20 UTC (4,167 KB)
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