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        <title>Data Quality Estimation Framework for Faster Tax Code Classification</title>
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        <namePart type="given">Ravi</namePart>
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            <namePart type="given">Shervin</namePart>
            <namePart type="family">Malmasi</namePart>
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            <namePart type="given">Oleg</namePart>
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            <namePart type="given">Nicola</namePart>
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            <namePart type="given">Surya</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Data Quality Estimation Framework for Faster Tax Code Classification
%A Kondadadi, Ravi
%A Williams, Allen
%A Nicolov, Nicolas
%Y Malmasi, Shervin
%Y Rokhlenko, Oleg
%Y Ueffing, Nicola
%Y Guy, Ido
%Y Agichtein, Eugene
%Y Kallumadi, Surya
%S Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F kondadadi-etal-2022-data
%X 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.
%R 10.18653/v1/2022.ecnlp-1.4
%U https://aclanthology.org/2022.ecnlp-1.4
%U https://doi.org/10.18653/v1/2022.ecnlp-1.4
%P 29-34

Markdown (Informal)

[Data Quality Estimation Framework for Faster Tax Code Classification](https://aclanthology.org/2022.ecnlp-1.4) (Kondadadi et al., ECNLP 2022)

• Data Quality Estimation Framework for Faster Tax Code Classification (Kondadadi et al., ECNLP 2022)

ACL

• Ravi Kondadadi, Allen Williams, and Nicolas Nicolov. 2022. Data Quality Estimation Framework for Faster Tax Code Classification. In Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5), pages 29–34, Dublin, Ireland. Association for Computational Linguistics.