Data Quality Estimation Framework for Faster Tax Code Classification

Abstract

This paper describes a novel framework to estimate the data quality of a collection of product descriptions to identify required relevant information for accurate product listing classification for tax-code assignment. Our Data Quality Estimation (DQE) framework consists of a Question Answering (QA) based attributevalue extraction model to identify missing attributes and a classification model to identify bad quality records. We show that our framework can accurately predict the quality of product descriptions. In addition to identifying lowquality product listings, our framework can also generate a detailed report at a category level showing missing product information resulting in a better customer experience.