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: this https URL

通过使用原型网络基于生成的标签描述和类别信息，结合混合注意力以减少噪音并捕获更多信息以实现更具有区别性的原型，我们提出了一种基于知识增强型注意力框架（KEAF）的多标签少样本学习（FSL）的属性值提取模型，并通过整合支持集和查询集的语义信息来学习动态阈值实现多标签推断。大量的实验和消融研究表明，KEAF 在少样本学习中的信息提取任务中优于其他 SOTA 模型。