SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning
This work addresses the challenge of accurately predicting helpfulness in e-commerce reviews, which is incremental as it builds on existing attention-based methods by refining attention mechanisms and adding contrastive learning.
The paper tackles the problem of multimodal review helpfulness prediction by proposing SANCL, which uses selective attention and natural contrastive learning to improve information capture and data correlation modeling, achieving state-of-the-art performance with lower memory consumption on benchmark datasets.
With the boom of e-commerce, Multimodal Review Helpfulness Prediction (MRHP), which aims to sort product reviews according to the predicted helpfulness scores has become a research hotspot. Previous work on this task focuses on attention-based modality fusion, information integration, and relation modeling, which primarily exposes the following drawbacks: 1) the model may fail to capture the really essential information due to its indiscriminate attention formulation; 2) lack appropriate modeling methods that take full advantage of correlation among provided data. In this paper, we propose SANCL: Selective Attention and Natural Contrastive Learning for MRHP. SANCL adopts a probe-based strategy to enforce high attention weights on the regions of greater significance. It also constructs a contrastive learning framework based on natural matching properties in the dataset. Experimental results on two benchmark datasets with three categories show that SANCL achieves state-of-the-art baseline performance with lower memory consumption.