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

• URL: http://arxiv.org/abs/2209.05040v3
• Date: Wed, 14 Sep 2022 15:42:56 GMT
• Title: SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning
• Authors: Wei Han, Hui Chen, Zhen Hai, Soujanya Poria, Lidong Bing
• Abstract summary: Multimodal Review Helpfulness Prediction (MRHP) aims to sort product reviews according to the predicted helpfulness scores. Previous work on this task focuses on attention-based modality fusion, information integration, and relation modeling. We propose SANCL: Selective Attention and Natural Contrastive Learning for MRHP.
• Score: 41.92038829041499
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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