Existing attribute-value extraction (AVE) models require large quantities of labeled data for training. However, new products with new attribute-value pairs enter the market every day in real-world e-Commerce. Thus, we formulate AVE in multi-label few-shot learning (FSL), aiming to extract unseen attribute value pairs based on a small number of training examples. We propose a Knowledge-Enhanced Attentive Framework (KEAF) based on prototypical networks, leveraging the generated label description and category information to learn more discriminative prototypes. Besides, KEAF integrates with hybrid attention to reduce noise and capture more informative semantics for each class by calculating the label-relevant and query-related weights. To achieve multi-label inference, KEAF further learns a dynamic threshold by integrating the semantic information from both the support set and the query set. Extensive experiments with ablation studies conducted on two datasets demonstrate that KEAF outperforms other SOTA models for information extraction in FSL. The code can be found at: https://github.com/gjiaying/KEAF

翻译：现有的属性值抽取（AVE）模型需要大量标注数据进行训练。然而，在实际电子商务场景中，每天都有带有新属性值对的新产品进入市场。因此，我们将AVE任务形式化为多标签少样本学习（FSL），旨在基于少量训练样本抽取未见过的属性值对。我们提出了一种基于原型网络的知识增强注意力框架（KEAF），利用生成的标签描述和类别信息来学习更具判别性的原型。此外，KEAF集成了混合注意力机制，通过计算标签相关权重和查询相关权重，减少噪声并捕获每个类别中更具信息性的语义。为实现多标签推理，KEAF进一步通过整合支持集和查询集的语义信息来学习动态阈值。在两个数据集上进行的大量实验和消融研究表明，KEAF在FSL信息抽取任务中优于其他最先进（SOTA）模型。代码可在以下地址获取：https://github.com/gjiaying/KEAF