Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction

Abstract

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

arXiv comment6 pages, 2 figures, published in CIKM 2023

References in corpus (6)

• Prototypical Networks for Few-shot Learning
• OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]
• PAM: Understanding Product Images in Cross Product Category Attribute Extraction
• OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision
• Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction
• Prototypical Networks for Multi-Label Learning