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.
翻译:随着电子商务的蓬勃发展,旨在根据预测的有用性分数对产品审查进行分类的多式审查帮助预测(MRHP)已成为一个研究热点。以前关于这项任务的工作侧重于基于关注的模式融合、信息整合和关系建模,这主要暴露了以下缺陷:(1)模型可能由于不加区分的注意配方而未能捕捉真正重要的信息;(2)缺乏充分利用所提供数据之间相互关系的适当建模方法。在本文件中,我们提议SANCL:MRHP的选择性关注和自然对抗学习。SANCL采取基于探测的战略,对更重要的区域实施高关注权重。它还根据数据集中的自然匹配特性构建了一个对比学习框架。关于两个基准数据集的实验结果显示,SANCL在记忆消耗量较低的情况下取得了最先进的基准性业绩。