  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.
