Attribute values of the products are an essential component in any e-commerce platform. Attribute Value Extraction (AVE) deals with extracting the attributes of a product and their values from its title or description. In this paper, we propose to tackle the AVE task using generative frameworks. We present two types of generative paradigms, namely, word sequence-based and positional sequence-based, by formulating the AVE task as a generation problem. We conduct experiments on two datasets where the generative approaches achieve the new state-of-the-art results. This shows that we can use the proposed framework for AVE tasks without additional tagging or task-specific model design.
翻译:产品属性值是任何电子商务平台的一个基本组成部分。 属性值采掘(AVE)处理从产品名称或描述中提取产品属性及其价值的问题。 在本文中,我们提议使用基因框架处理AVE的任务。 我们提出两种基因模式,即:单词序列和定位序列模式,将AVE的任务作为一个世代问题来拟订。我们实验了两个数据集,在这两个数据集中,基因化方法取得了新的最新结果。这显示,我们可以使用拟议的AVE任务框架,而无需额外的标记或特定任务模式设计。