Product attribute value extraction is an important task in e-Commerce which can help several downstream applications such as product search and recommendation. Most previous models handle this task using sequence labeling or question answering method which rely on the sequential position information of values in the product text and are vulnerable to data discrepancy between training and testing. This limits their generalization ability to real-world scenario in which each product can have multiple descriptions across various shopping platforms with different composition of text and style. They also have limited zero-shot ability to new values. In this paper, we propose a multi-task learning model with value generation/classification and attribute prediction called JPAVE to predict values without the necessity of position information of values in the text. Furthermore, the copy mechanism in value generator and the value attention module in value classifier help our model address the data discrepancy issue by only focusing on the relevant part of input text and ignoring other information which causes the discrepancy issue such as sentence structure in the text. Besides, two variants of our model are designed for open-world and closed-world scenarios. In addition, copy mechanism introduced in the first variant based on value generation can improve its zero-shot ability for identifying unseen values. Experimental results on a public dataset demonstrate the superiority of our model compared with strong baselines and its generalization ability of predicting new values.

翻译：产品属性值提取是电子商务中的重要任务，可帮助产品搜索和推荐等多类下游应用。以往大多数模型使用序列标注或问答方法处理该任务，这些方法依赖产品文本中值的序列位置信息，且易受训练与测试数据差异的影响。这限制了它们在真实场景中的泛化能力——实际环境中每个产品可能在不同购物平台拥有多种描述，且文本构成和风格各异。同时，这些方法对新值缺乏零样本能力。本文提出一种多任务学习模型JPAVE，通过值生成/分类与属性预测联合建模，无需依赖文本中值的位置信息即可预测属性值。此外，值生成器中的复制机制与值分类器中的值注意力模块通过仅关注输入文本的相关部分、忽略导致数据差异的信息（如文本句式结构），有效解决了数据差异问题。我们还针对开放世界和封闭世界场景设计了两种变体。其中，基于值生成的第一种变体引入的复制机制可提升识别未见值的零样本能力。在公开数据集上的实验结果表明，相比强基线模型，本模型具有优越性，并能有效泛化预测新值。