SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning

Anonymous

Abstract: With the boom of e-commerce, Multimodal Review Helpfulness Prediction (MRHP) that identifies the helpfulness score of multimodal product reviews 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 takes 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.

Paper Type: long