  E-commerce platforms categorize their products into a multi-level taxonomy
tree with thousands of leaf categories. Conventional methods for product
categorization are typically based on machine learning classification
algorithms. These algorithms take product information as input (e.g., titles
and descriptions) to classify a product into a leaf category. In this paper, we
propose a new paradigm based on machine translation. In our approach, we
translate a product's natural language description into a sequence of tokens
representing a root-to-leaf path in a product taxonomy. In our experiments on
two large real-world datasets, we show that our approach achieves better
predictive accuracy than a state-of-the-art classification system for product
categorization. In addition, we demonstrate that our machine translation models
can propose meaningful new paths between previously unconnected nodes in a
taxonomy tree, thereby transforming the taxonomy into a directed acyclic graph
(DAG). We discuss how the resultant taxonomy DAG promotes user-friendly
navigation, and how it is more adaptable to new products.

Computation and Language;Machine Learning