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

TL;DR

This paper introduces KEAF, a novel framework that enhances multi-label few-shot product attribute-value extraction by leveraging knowledge and attention mechanisms, significantly improving performance on e-Commerce datasets.

Contribution

The paper proposes KEAF, a knowledge-enhanced attentive framework based on prototypical networks, for multi-label few-shot attribute-value extraction in e-Commerce.

Findings

KEAF outperforms state-of-the-art models on two datasets.

Incorporating label descriptions and category info improves prototype discrimination.

Dynamic threshold learning enhances multi-label inference accuracy.

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