SANCL：基于选择性注意和自然对比学习的多模态评论有用性预测
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning
摘要：
随着电子商务的蓬勃发展，多模态评论有用性预测（MRHP）成为研究热点，其目的是根据预测的有用性分数对产品评论进行排序。以往的研究主要集中在基于注意力的模态融合、信息整合和关系建模等方面，主要存在以下不足：1）注意力的不加选择性导致模型无法捕捉到真正重要的信息; 2）缺乏适当的建模方法来充分利用数据之间的相关性。在本文中，我们提出了SANCL：选择性注意和自然对比学习的MRHP。SANCL采用基于探测器的策略，对重要性更高的区域实施高关注权重。它还构建了一个对比学习框架的基础上自然匹配属性的数据集。在两个基准测试数据集上的实验结果表明，SANCL以较低的内存消耗实现了最先进的基线性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
