Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value
  Extraction
Jiaying Gong Wei-Te Chen Hoda Eldardiry
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