Data Quality Estimation Framework for Faster Tax Code Classification (2022)

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 attribute value 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 low-quality product listings, our framework can also generate a detailed report at a category level showing missing product information resulting in a better customer experience.

## introduction as a global tax compliance company, avalara enables businesses to use the correct sales tax rate by mapping their product catalogs to a tax code taxonomy built by avalara. the tax codes, in turn, inform the tax calculation engine how to apply the tax for a transaction. this mapping process is very laborious today due to many reasons. one of the main challenges is the quality of the product catalog data we receive from customers. many times, this data is quite vague and noisy. this can be caused by many factors. 1. not enough context about the business: for tax code classification, we only receive a collection of product titles. this product information does not give enough context about the industry in general, causing problems in tax code mapping, especially if the language in the product information is ambiguous. this lack of context results in the mapping team having to talk to the business to get more information about the business and the corresponding industry. this is a very tedious process requiring a lot of manual effort, causing delays in the customer onboarding process. a model including these factors to identify the quality of product titles would help the mapping team request additional information for those products from the business and accelerate the onboarding process for that customer. in this paper, we describe a novel data quality estimation framework which businesses can interact with and provide all relevant information required to map all entries in a product catalog to the corresponding tax codes. iteratively, the tool can map input product records to tax codes, identify low quality records and present pertinent questions to the user for the bad records. the tool repeats the process until all records are fixed, and the mappings are complete for the entire product catalog. next, we discuss our data quality estimation framework. we then describe our methodology and experiments followed by relevant recent work. ## data quality estimation framework in this section, we present details of the data quality estimation framework. the framework includes a tax code classification model, an attribute-value extraction model, and a quality assessment model. next, we will discuss each of these components in detail. ## experimental results in this section, we present the evaluation of both the attribute-value extraction and the quality assessment models. ## conclusion we presented a novel data quality estimation framework for the e-commerce domain that can identify product listings with incomplete information. the framework includes a question answering based attribute-value extraction model trained on the mave dataset. we prove that our framework can reliably identify inadequate product listings resulting in faster tax code classification. beyond mapping products to tax codes, our framework is applicable to services (in fact, our toplevel categories already include a services group), as well as, utilities/energy, or in general any domain where items can be described in terms of attributes and values. we are applying this framework to other tax code ontologies like the harmonized commodity description and coding system (hs) which provides codes for traded products as part of international transactions.