  Extracting structured knowledge from product profiles is crucial for various
applications in e-Commerce. State-of-the-art approaches for knowledge
extraction were each designed for a single category of product, and thus do not
apply to real-life e-Commerce scenarios, which often contain thousands of
diverse categories. This paper proposes TXtract, a taxonomy-aware knowledge
extraction model that applies to thousands of product categories organized in a
hierarchical taxonomy. Through category conditional self-attention and
multi-task learning, our approach is both scalable, as it trains a single model
for thousands of categories, and effective, as it extracts category-specific
attribute values. Experiments on products from a taxonomy with 4,000 categories
show that TXtract outperforms state-of-the-art approaches by up to 10% in F1
and 15% in coverage across all categories.
